Creating a Feedback Loop for Tech Innovations

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 Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly.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 An AI model that never learns from its users is already outdated.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.

Feedback loops are AI’s compound interest engine.. if you skip them and your AI performance will just erode over time. Too many roadmaps punt on serious evals because “models don’t hallucinate as much anymore” or “we’ll tighten it up later.” Be wary of those that say this, they really aren't serious practitioners. Here is the gold standard we run for production AI implementation at Bottega8: 1. Offline evals (CI gatekeeper): A lightweight suite of prompt unit tests, RAGAS faithfulness checks, latency, and cost thresholds runs on every PR. If anything regresses, the build fails. 2. RLHF, internal sandbox: A staging environment where we hammer the model with synthetic edge cases and adversarial red team probes. 3. RLHF, dogfood: Real users and real tasks. We expose a feedback widget that decomposes each output into groundedness, completeness, and tone so our labelers can triage in minutes. 4. RLHF, virtual assistants: Contract VAs replay the week’s top workflows nightly, score them with an LLM as judge, and surface drift long before customers notice. 5. Shadow traffic and A/B canaries: Ten percent of live queries route to the new model, and we ship only when conversion, CSAT, and error budgets clear the bar. The result is continuous quality and predictable budgets.. no one wants mystery spikes in spend nor surprise policy violations. If your AI pipeline does not fail fast in code review and learn faster in production, it is not an engineering practice, it is a gamble. There's enough eng industry best practice now with nearly three years of mainstream LLM/GenAI adoption. Happy building and let's build AI systems that audit themselves and compound insight daily.

Microsoft just promoted 4 sales leaders to EVP. The press release buried the real story. One line stood out: "Keep the feedback loop between customers and product decisions as small as possible." Read that again. Microsoft—a company with 220,000 employees—is restructuring its entire sales leadership to shrink the distance between what customers need and what product builds. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗖𝗥𝗢𝘀: Microsoft isn't doing this for fun. They're doing it because "AI is being adopted at extraordinary speed, and customers expect these capabilities to come to life in their business faster than ever before." Translation: The old model—sales captures feedback, passes it to product, product builds it 18 months later—is dead. Customers won't wait. Competitors won't wait. Your org structure can't wait either. 𝗧𝗵𝗲 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝗻 𝗺𝗼𝘀𝘁 𝘀𝗮𝗹𝗲𝘀 𝗼𝗿𝗴𝘀: • Reps hear what customers actually need • That insight gets buried in CRM notes nobody reads • Product builds features based on internal roadmaps • Customers churn because their problems never get solved • Everyone blames "alignment issues" The loop is too long. The signal gets lost. Deals die in the gap. 𝗪𝗵𝗮𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀: When you elevate sales leaders to EVP and give them direct lines to product strategy, you're not just promoting people. You're compressing the feedback loop by design. Customer pain → Sales leadership → Product decision. No 6-month committee reviews. No "we'll add it to the backlog." No lost-in-translation moments. 𝗧𝗵𝗲 𝗖𝗥𝗢 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: How long does it take for customer feedback from your sellers to influence a product decision at your company? If the answer is "months" or "I don't know," your feedback loop is a competitive liability. The companies winning in AI aren't just deploying faster. They're learning faster. And learning speed is a function of feedback loop length. How compressed is your customer-to-product feedback loop?

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

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

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

You can spend millions on new tech, but without this one skill, you're part of the 70% that fail. Ever watched a child resist trying new food? That's exactly how most employees feel about new technology at work. I learned this the hard way while leading digital changes in my team. The game changer wasn't fancy software, it was understanding how my team felt. Here's the exact playbook that turned my team's tech fear into enthusiasm: 1. Listen first, act later. When team members worry about losing their jobs to automation, show them how the new tools will make their work easier, not take it away. Schedule dedicated 1:1 sessions to document concerns. 2. Keep talking, keep sharing. Set up structured communication channels, bi-weekly tech updates and anonymous feedback systems. 3. Take baby steps. No one learned to run before walking. Give your team time to learn new tools at their own pace. Break training into short, digestible 15-minute daily modules focusing on immediate-use features. 4. Celebrate small victories. Create a weekly "Tech Win" spotlight in team meetings to recognize progress. 5. Know yourself first. As a leader, if you're stressed about change, your team will feel it too. Use established change management frameworks to assess and manage your own readiness for change. The success of digital initiatives isn't measured by technological efficiency, but by how well teams adapt and thrive in their new environment. What's the biggest challenge you've faced when implementing new technology in your team? #Leadership #Growth #Change #Success

That’s the thing about feedback—you can’t just ask for it once and call it a day. I learned this the hard way. Early on, I’d send out surveys after product launches, thinking I was doing enough. But here’s what happened: responses trickled in, and the insights felt either outdated or too general by the time we acted on them. It hit me: feedback isn’t a one-time event—it’s an ongoing process, and that’s where feedback loops come into play. A feedback loop is a system where you consistently collect, analyze, and act on customer insights. It’s not just about gathering input but creating an ongoing dialogue that shapes your product, service, or messaging architecture in real-time. When done right, feedback loops build emotional resonance with your audience. They show customers you’re not just listening—you’re evolving based on what they need. How can you build effective feedback loops? → Embed feedback opportunities into the customer journey: Don’t wait until the end of a cycle to ask for input. Include feedback points within key moments—like after onboarding, post-purchase, or following customer support interactions. These micro-moments keep the loop alive and relevant. → Leverage multiple channels for input: People share feedback differently. Use a mix of surveys, live chat, community polls, and social media listening to capture diverse perspectives. This enriches your feedback loop with varied insights. → Automate small, actionable nudges: Implement automated follow-ups asking users to rate their experience or suggest improvements. This not only gathers real-time data but also fosters a culture of continuous improvement. But here’s the challenge—feedback loops can easily become overwhelming. When you’re swimming in data, it’s tough to decide what to act on, and there’s always the risk of analysis paralysis. Here’s how you manage it: → Define the building blocks of useful feedback: Prioritize feedback that aligns with your brand’s goals or messaging architecture. Not every suggestion needs action—focus on trends that impact customer experience or growth. → Close the loop publicly: When customers see their input being acted upon, they feel heard. Announce product improvements or service changes driven by customer feedback. It builds trust and strengthens emotional resonance. → Involve your team in the loop: Feedback isn’t just for customer support or marketing—it’s a company-wide asset. Use feedback loops to align cross-functional teams, ensuring insights flow seamlessly between product, marketing, and operations. When feedback becomes a living system, it shifts from being a reactive task to a proactive strategy. It’s not just about gathering opinions—it’s about creating a continuous conversation that shapes your brand in real-time. And as we’ve learned, that’s where real value lies—building something dynamic, adaptive, and truly connected to your audience. #storytelling #marketing #customermarketing

If you've got a new service, or product, or if you enter a new vertical, even if your partners are ushering you into their market, expect skepticism. Even with the best partners advocating for you, decision-makers may hesitate and many companies will put you at the bottom of their priority list until you can prove your value. It’s crucial to get traction quickly, or risk being overlooked. Here’s what I would do to break through that initial skepticism and gain momentum: 1. Pilot Programs: Offering a limited-time trial can help, but only if it's designed to deliver clear value from day one. - Set clear success metrics with your customer before the pilot begins. Establish measurable outcomes like improved productivity, user engagement, or cost savings. - Don’t just give them the product—ensure their teams are trained and equipped to use it effectively during the trial. This maximizes the chance of success and measurable impact. 2. Feedback Loops: Regular, structured communication with your partners and customers is key to refining your offering. - Set up bi-weekly check-ins to gather both quantitative data (usage rates, performance metrics) and qualitative feedback (user experience, pain points). - Use this feedback to adapt your approach in real time. Whether it’s tweaking features, adjusting pricing, or improving support, make sure you’re iterating based on what you hear. 3. Case Studies: Success stories build trust and reduce uncertainty for potential customers. - Create detailed case studies highlighting real results from your pilot programs or early adopters. Focus on specific benefits—whether that’s operational efficiency, cost savings, or user satisfaction. -Share these case studies with future prospects to showcase the value and credibility of your service. Timely, relevant examples can turn a hesitant prospect into a committed customer. Gaining traction with a new service takes time, but with the right strategies you can overcome skepticism and build momentum.

Over the past year or two, I’ve had the opportunity to work closely with innovation ecosystems through our Strategy Hub Qatar projects, an area that has increasingly captured my interest, particularly for the potential it holds to drive real-world impact. What I’ve noticed is an under-tapped opportunity to further strengthen these ecosystems by anchoring them in demand-led, partnership-driven innovation, where startups are not building in isolation, but are closely linked to clearly defined industry needs. When large industry players are engaged as active problem owners and potential adopters, the dynamic becomes far more powerful. Startups are able to design solutions that are grounded in real, validated challenges and closely aligned with operational realities. In this context, design thinking helps anchor innovation in real user needs, encouraging early testing and iteration to shape solutions that are practical and implementable. The real opportunity lies in creating a clear pathway from solution to adoption, where ideas are not only developed, but tested, refined, and ultimately taken forward by those they are designed for. This makes innovation more targeted, more responsive to local needs, and far more likely to scale. There is a compelling opportunity to deepen this model by fostering more intentional partnerships between industry and startups, creating a more connected, closed-loop system where challenges inform innovation, and innovation feeds directly back into industry. In this model, large players benefit from agile, locally developed solutions, while startups gain clearer pathways to adoption and scale. Realizing this potential requires embedding the right enabling mechanisms within these partnerships, particularly procurement and funding models that support experimentation, iteration, and ultimately, implementation. #InnovationEcosystems #PublicPrivatePartnerships #StartupEcosystem #IndustryPartnerships #DesignThinking