AI In Employee Feedback

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

Do your performance reviews still feel like guesswork? The latest SAP SuccessFactors release quietly introduced something game‑changing: AI that actually helps you write feedback and plan goals. Here’s what caught my eye. • The new Performance & Goals module now suggests comments based on the skill and rating you choose. No more staring at a blank box wondering how to phrase constructive criticism. • It generates “performance insights” that sift through an employee’s data and summarize strengths, achievements, and areas to improve. In other words, you walk into one‑to‑ones with a clear picture and a fairer perspective. • Sentiment analysis flags negative or mixed feedback in 360‑degree reviews, so you know where to focus your coaching. • Preparation time for compensation discussions drops by 90% because AI surfaces the right talking points, and overall performance goal‑setting is 80% faster. What this really means is that AI is moving from buzzword to practical tool. It’s taking the busywork out of reviews and letting managers spend more time on real conversations. And it’s doing it while employees still feel seen and fairly evaluated. I’m curious: would you trust AI to help shape feedback and compensation discussions? Have you tried any of these tools yet? Share your experiences — or tag a colleague who should weigh in. #SAPSuccessFactors #PerformanceManagement #AIinHR #PeopleAnalytics #FutureOfWork

Over the past 10 weeks, I’ve interviewed 35 talent and learning leaders at Fortune 1000 companies for a report I’ll be releasing this fall. One of my favorite questions has been the very first one: 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐭𝐨𝐩 𝐭𝐡𝐫𝐞𝐞 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰?” With 105 priorities and counting, the responses vary widely given differences in industry, scope, and role (VP of Learning, talent, talent management, leadership development) but here is a slice of what has been shared so far: ➡️ AI and work transformation: Clarify what AI means for the workforce, its implications for roles, and how teams can adopt it to accelerate development and efficiency. ➡️ AI Coaching Pilot: Launch an AI-powered coaching pilot program across the organization to scale leadership development support. ➡️ Generative AI Upskilling: Upskill employees and leaders to effectively use generative AI in day-to-day work ➡️ Future of Work & Workforce Planning: Prepare for disruptions to job architecture by integrating human and digital workforces. Rethink responsibilities, structures, and collaboration models. ➡️ Change management: Embed change management capabilities at all levels, particularly around AI adoption. ➡️ New leadership Behaviors: Equip leaders with new capabilities to thrive in a changing environment, including adaptability, resilience, and the ability to lead in an AI-augmented workplace. ➡️ Skills and Career Paths - Creating paths by prioritized skills in our organization ➡️ Rethinking the Function: Redesign the talent and learning function to reflect disruption caused by AI ➡️ Change Leadership: Navigate a period of executive turnover and transition by stabilizing the leadership team, clarifying roles, and building confidence with functional business leaders. ➡️ Facilitating Connection: Partnering with our employee experience and workplace teams to use in-office team days for learning and connection ➡️ Linking Performance and Development: Redesign performance processes to connect directly to development, helping employees understand what growth means in practical and tangible terms. ➡️ Manager Development: Continue to strengthen manager capability and resources, ensuring managers are equipped to drive performance and support employee development ➡️ VP and SVP Development: Support and accelerate the growth of new vice presidents and senior vice presidents as they step into expanded leadership roles. ➡️ Building a Leadership Bench : Develop and execute a strategy for strengthening the leadership bench, with a focus on preparing our Top 200 leaders ➡️ AI/Learning : Using AI internally within the learning function and focusing on key skills in AI for client-facing practitioners ➡️ Academies For AI/Data Roles: Developing and rolling out an academy for our AI & Data Product Employees I’d love to hear your perspective: What stands out most to you about this list, or what themes are you seeing in this list?

That time I asked Chat GPT for feedback on my coaching style 🫣 After reading the Harvard Business Review article “How AI Helped Executives Improve Communication” by Dr Katharina Lange and José Parra Moyano, I was intrigued. The premise was simple: leaders recorded real conversations, ran them through AI, and got objective feedback on their coaching style. Naturally, I got curious. What would I learn if I applied the same idea to one of my own coaching conversations? As the host of the Coaching Real Leaders podcast, I actually have an unusual advantage: some of my coaching sessions are recorded and transcribed. So I pulled up a recent episode (Season 9, Episode 5 — “How Do I Co-lead with a Challenging Partner” if you’re interested) and ran it through AI, following the same process described in the article. ✅ I lean on reflective, forward-moving questions (good—I sometimes second-guess that). ✅ I balance confronting + informative styles without taking over (nice validation). ✅ My coaching flow is consistent: reflect → reframe → act. ✅ I could make more space for deeper emotions (fair point—I usually let them unfold on their own). Nothing shocking—but a great reminder of the power of feedback in a profession that often operates behind closed doors. Of course, I know most coaches can’t just drop conversations into AI for analysis, given confidentiality. I was only able to do this because my podcast episodes are already shared publicly (with client permission). But that doesn’t mean feedback isn’t accessible—it just takes creativity. Like listening back to a recording (with permission), inviting objective peer review, or using structured reflection tools, like Heron’s Six Intervention Styles as mentioned in the article. All of these are ways to see ourselves more clearly. So, fellow coaches, I’m curious: - When’s the last time you listened to yourself coach? - What patterns would you notice? - How do you know if your conversations are having impact? Coaching is a craft. Feedback is one of the sharpest tools we have to keep it honed. I’d love to hear how you get feedback on your coaching—drop your tips in the comments. #ExecutiveCoaching #CoachingRealLeaders #HBR #SelfAwareness #CoachingFeedback #AI

💬Now is the time for leaders to rethink job descriptions. Many believe that updating job descriptions every 3-5 years is sufficient. 🐌 Those days are gone. ⏩ You should be reassessing jobs every 4-6 months. Focus on the human elements that Al cannot replicate: ✅ creativity ✅ strategy ✅ interpersonal skills Then, thoughtfully redesign roles to use Al's strengths so that there’s more time to apply those human elements! This is not about replacing jobs, but reimagining them to foster innovation and drive business growth. What does this practically look like? 🖥️ IT As AI takes over routine coding and troubleshooting tasks, IT professionals can focus on designing complex, strategic IT architectures, cybersecurity innovations, and facilitating the integration of new technologies within the company. 📊 Finance AI can handle data analysis and report generation. Finance experts can shift towards interpreting this data for strategic decision-making, focusing on financial forecasting and advising on investment opportunities leveraging AI-driven insights. 🤝 Sales With AI handling initial customer inquiries and lead qualification, sales representatives can dedicate more time to understanding client needs, building relationships, and developing customized solutions that truly resonate with each customer. 🔄 Operations As AI streamlines logistics and inventory management, operations personnel can concentrate on optimizing supply chain strategy, vendor relations, and sustainability practices. 👥 HR AI can manage payroll, benefits administration, and resume screening. HR professionals can then focus on employee engagement strategies, professional development programs, and fostering company culture. 🎨 Marketing With AI taking on market analysis and targeted advertising, marketers can pivot to crafting more compelling brand narratives, innovative campaign strategies, and engaging content that speaks to human emotions and experiences. ⚖️ Legal AI can assist in document review and due diligence processes. Legal professionals can focus on complex negotiations, strategic counseling, and providing personalized legal advice where human judgment is critical. 📦 Supply Chain AI could handle demand forecasting and inventory optimization. Supply chain experts can then work on strategic partnerships, resilience planning, and exploring new market opportunities. —- The savviest employees have learned new ways of working already. How about you? Have you told anyone that you no longer work the same way? Share how you’re working differently now 👇🏻 #Innovation #Growth #AI #management #FutureOfWork

LLMs are optimized for next turn response. This results in poor Human-AI collaboration, as it doesn't help users achieve their goals or clarify intent. A new model CollabLLM is optimized for long-term collaboration. The paper "CollabLLM: From Passive Responders to Active Collaborators" by Stanford University and Microsoft researchers tests this approach to improving outcomes from LLM interaction. (link in comments) 💡 CollabLLM transforms AI from passive responders to active collaborators. Traditional LLMs focus on single-turn responses, often missing user intent and leading to inefficient conversations. CollabLLM introduces a :"Multiturn-aware reward" system, apply reinforcement fine-tuning on these rewards. This enables AI to engage in deeper, more interactive exchanges by actively uncovering user intent and guiding users toward their goals. 🔄 Multiturn-aware rewards optimize long-term collaboration. Unlike standard reinforcement learning that prioritizes immediate responses, CollabLLM uses forward sampling - simulating potential conversations - to estimate the long-term value of interactions. This approach improves interactivity by 46.3% and enhances task performance by 18.5%, making conversations more productive and user-centered. 📊 CollabLLM outperforms traditional models in complex tasks. In document editing, coding assistance, and math problem-solving, CollabLLM increases user satisfaction by 17.6% and reduces time spent by 10.4%. It ensures that AI-generated content aligns with user expectations through dynamic feedback loops. 🤝 Proactive intent discovery leads to better responses. Unlike standard LLMs that assume user needs, CollabLLM asks clarifying questions before responding, leading to more accurate and relevant answers. This results in higher-quality output and a smoother user experience. 🚀 CollabLLM generalizes well across different domains. Tested on the Abg-CoQA conversational QA benchmark, CollabLLM proactively asked clarifying questions 52.8% of the time, compared to just 15.4% for GPT-4o. This demonstrates its ability to handle ambiguous queries effectively, making it more adaptable to real-world scenarios. 🔬 Real-world studies confirm efficiency and engagement gains. A 201-person user study showed that CollabLLM-generated documents received higher quality ratings (8.50/10) and sustained higher engagement over multiple turns, unlike baseline models, which saw declining satisfaction in longer conversations. It is time to move beyond the single-step LLM responses that we have been used to, to interactions that lead to where we want to go. This is a useful advance to better human-AI collaboration. It's a critical topic, I'll be sharing a lot more on how we can get there.

A Director of UX at a SaaS company recently shared a painful calculation with me: Their team of 3 researchers spent 75% of their time on manual analysis. At an average salary of $150K, that's nearly $300K annually spent on analyzing data. But the bigger cost? Critical product decisions made without insights because "we can't wait for research." Most UX and product teams are trapped in a costly cycle of inefficiency: Conduct user interviews → Spend 30+ hours manually analyzing → Create a report → Make decisions based on gut feeling before the report is ready. After watching UX teams struggle with this for years, I've identified the core problem: research insights are treated as artifacts, not conversations. This is why we built AI Wizard into Looppanel - a conversational research companion that transforms how teams extract value from user research. Instead of static reports and manual analysis, AI Wizard allows anyone to simply ask: "What pain points did users mention about the onboarding process?" "Summarize the key recommendations users suggested for improving the checkout flow." "What were the main differences in how novice users versus power users approached this task?" You start by selecting from templates like Pain Points, Recommendations, or Summary. AI Wizard instantly analyzes your project data and engages in a natural conversation - complete with follow-up questions to dig deeper into specific areas. The way I see it, AI Wizard helps solve 3 critical problems: 1. The speed-to-decision problem Waiting weeks for analysis means missing decision windows. AI Wizard delivers TLDR overviews in seconds, not days. 2. The iteration problem No more spending time on data again because of a follow-up question. Answer unexpected stakeholder questions on the spot instead of scheduling another week of analysis 3. The tailored communication problem Automatically format the same insights for different audiences: executives get metrics, designers get details, all without rebuilding presentations. With AI Wizard, your team can: → Start conversations with templates like Pain Points, Recommendations, or Summary → Ask follow-up questions to dig deeper → Get insights from across your entire research repository in seconds → Democratize access to insights throughout your organization Will your team be leading this transformation or catching up to it? If you want to make the shift, sign up for a personalized demo here: https://bit.ly/42PEOlX

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.

Last week, I shared insights from the AI in Action: Practical Insights for L&D session I facilitated for the Australian Institute of Training & Development - AITD in Canberra. We explored how L&D professionals are using AI, examined case studies from the Learning Uncut Podcast, and co-created good practices for AI adoption. A key part of the session was moving beyond discussion and into hands-on experimentation with Generative AI. Participants had the opportunity to apply AI to real-world L&D scenarios, working through practical activities designed to enhance their skills, solve work challenges, and improve processes. Here are three activities we explored: 💡 Skill development planning – Participants used AI to create a 30-day professional development plan tailored to specific personal learning needs. AI helped structure their goals, recommend relevant resources, and outline ways to track progress. 💡 Work challenge coaching – AI acted as a coaching tool, asking probing questions to help participants reflect on and navigate a current work challenge. The AI-generated insights, potential actions, and reflection questions supported deeper problem-solving. 💡 Work process improvement – Participants explored how AI could streamline or enhance a regular work task, brainstorming with AI to identify efficiency improvements, potential benefits, and workflow considerations. The intent of the selected activities was to give L&D practitioners attending a taste of not only how they could use AI to support their own development and improvement, but spark ideas for how they could introduce similar approaches to others in their organisation. These exercises reinforced that AI can be a valuable tool for enhancing L&D effectiveness - but only when paired with human expertise, critical thinking, and contextual adaptation. If you’re curious to try these activities yourself, you can access the full prompt document here: https://lnkd.in/dZKzi2A6 I am interested to hear if you try one of these - how did you find the activity? #LearningAndDevelopment #AI #GenerativeAI #ProfessionalDevelopment #ChatGPT

As a leader, feedback is easy to get wrong. So I taught an AI how to challenge me. Ahead of a high-stakes conversation, I pulled together 25 pages of research on feedback frameworks and communication styles. Then I asked Gemini to act as a coach, to pressure-test how I planned to deliver the message. It flagged moments where my phrasing could trigger defensiveness, suggested better prompts to invite reflection, and helped me land on language that was clear without being harsh. A few examples: → “What message were you hoping came through?” Instead of: “Why did you present it this way?” → “What can we do next time to make this clearer?” Instead of: “You missed the point.” This wasn’t about offloading responsibility to AI. It was about raising the bar for how I show up. AI didn’t write the feedback for me. It made me a sharper, calmer, more effective leader when it mattered most. We talk a lot about how AI can save time. But its real power lies in helping us prepare better, listen harder, and speak in ways that build trust, even in tough moments. How do you use AI as your thought partner?