Using Feedback Loops For Tech Strategy Refinement

The Rollout Is Just the Starting Line. Now Listen, Learn, and Adapt Rolling out new technology isn’t a finish line; it’s where the real work begins. The first few weeks post-launch are critical. That’s when friction points surface, shortcuts emerge, and usage patterns reveal what’s working (and what’s not). That’s why smart leaders build robust feedback loops from day one, not as an afterthought. 📢 Create clear, no-hassle ways for employees to share real-time feedback (on usability, integration gaps, or where they’re getting stuck). 🔁 Commit to action: Based on that input, adjust workflows, refine dashboards, or tweak configurations. Even small changes show you’re listening. 🎯 Provide targeted follow-up training, focused on what people need help with, not what the vendor’s onboarding assumed. This isn’t about perfection on day one, it’s about building a system that adapts quickly and aligns with real user experience. Because when employees feel heard and supported, adoption doesn’t just stick, it accelerates. How are you closing the loop between user feedback and system evolution? If you need help, you can always talk to Digital Transformation Strategist.

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.

In just 30 days, defects dropped, morale increased... And our roadmap conversations shifted from “𝘐 𝘵𝘩𝘪𝘯𝘬 𝘸𝘦 𝘴𝘩𝘰𝘶𝘭𝘥 ” to “𝘏𝘦𝘳𝘦’𝘴 𝘸𝘩𝘢𝘵 𝘸𝘦 𝘬𝘯𝘰𝘸.” Every engineering leader wants to get the most out of their team, but it’s easy to lose sight of what really drives them: feedback. I learned this the hard way. I launched a product that was all hype, but there was nothing from the users. I quickly realized: engineers need to see the impact of their work. Without feedback, it’s all guesswork and that leads to frustration. Here’s how I turned things around: 𝐒𝐡𝐚𝐫𝐞 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐓𝐚𝐥𝐤: I started recording customer calls and sharing the raw moments, the “wow!” reactions and frustrations. Engineers connect with that energy way more than bullet points. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭’𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Instead of just letting support tickets pile up, we held quick 5-minute debriefs each sprint to highlight recurring issues that specs missed. 𝐎𝐧-𝐂𝐚𝐥𝐥 𝐄𝐦𝐩𝐚𝐭𝐡𝐲: Every quarter, we had an engineer join the on-call rotation. Waking up at 3 AM to fix a bug you wrote? That’s a whole new level of ownership. 𝐈𝐧𝐯𝐨𝐥𝐯𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐄𝐚𝐫𝐥𝐲: Before features hit Jira, we brought engineers into discovery calls. Hearing the “why” from customers helped them think critically before the code was even written. The results? 30 days later, defects dropped, morale improved, and our roadmap shifted from gut feeling guesses to data driven decisions. Feedback loops are the key to growth. Start today.

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.

Momentum in startups isn't about speed. It's about creating loops that reinforce each other. Early on, I learned that progress often feels linear and exhausting. Each week, it seemed like I was relearning the same lessons because insights weren't circulating. Marketing, product, and sales were working in isolation. But then, I discovered the power of feedback loops. Sales calls refined our pitch. The pitch refined our positioning. Positioning attracted the right users. And the right users gave better feedback. That's when our efforts started to pay off twice. Learning began to recycle, and momentum wasn't about moving faster. It was about less wasted motion. Here's how you can engineer compounding growth: Close loops faster by shortening feedback cycles. Make insights visible by writing, sharing, and documenting. Reduce resets by sharing context across teams. The real signal of success? You stop solving the same problems twice.

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.

The #1 reason people don't use AI in their workflows (and how to fix it) In a recent Supra Insider podcast, Jacob Bank from Relay.app shared a powerful playbook for effective AI implementation. His critical insight: "The main reason people don't use AI in practice right now is not because they haven't heard of it, not because they don't think it's cool... just because they can't trust it to do work on their behalves." The solution? Human-in-the-loop design. Instead of viewing AI as "fully automated or not," successful implementations create thoughtful checkpoints where humans remain in control: 1/ Plan transparency Before executing, AI should communicate its approach to the task. This creates confidence by letting users understand what will happen. Without this step, users fear uncontrolled actions like "writing 5,000 emails to every customer individually" or running up costs unnecessarily. Examples: "Here's how I'll tackle this task and where I'll need your input." 2/ Refinement opportunities Create explicit moments where humans can guide the AI's work while it's in progress. These aren't just approval checkpoints but collaborative interactions. These refinement stages are perfect for content creation, telling the AI to "emphasize this part of the conversation more, this part less, go back and try again." Examples: ↳ "This looks good, but emphasize this part more" ↳ "These results need context from last quarter" ↳ "You're missing an important constraint" 3/ Quality assurance gates Establish critical approval points that cannot be bypassed before final output. For successful AI workflows like LinkedIn content creation, never let AI publish directly. For important workflows, multiple QA checkpoints are essential - first reviewing the draft, then refining for polish, and finally a human edit before publishing. Examples: ↳ "Review this draft before sending" ↳ "Confirm these metrics are accurate" ↳ "Approve this selection of priority items" 4/ Outcome verification Close the loop by providing feedback on results to improve future performance. This step makes AI tools progressively more valuable over time. Use this approach to refine content workflows by analyzing which posts perform well and feeding that data back into the system. Examples: ↳ "The approach worked, but next time include X" ↳ "This missed the mark because of Y" ↳ "This exceeded expectations, let's rely on it more" Even with perfect prompts, AI drafts typically only get "80% of the way to the quality bar" needed for publication. The companies winning with AI aren't eliminating humans from the process. They're creating thoughtful collaboration points that leverage the strengths of both. Where are you implementing human-in-the-loop design in your AI workflows? What checkpoints have you found most valuable?

It’s really easy to say, “We have a strategy,” but it’s another thing to see that plan and approach truly shape day-to-day decisions without you having to remind people constantly. You might have a great vision, spelled out in different formats, but still find your teams are guessing on what matters most. Or maybe you’ve got clear priorities, but they never quite connect to the bigger narrative of why the company exists, where it’s heading, or what’s keeping your leadership team up at night. That disconnect shows up as scattered initiatives, work you have no idea why it exists, or leaders talking in a completely different way than everyone else. So why does this happen? One culprit is mixing up “strategy” with “a list of important goals.” Good strategy isn’t just a set of targets. It’s the framework that guides trade-offs. If you’re not making conscious choices about what you won’t do, then it’s not really strategy (it’s a to-do list). It doesn’t help when you're operating at hyper-speed. You’re juggling fires, random pivots, and new opportunities every week. So anything static can feel outdated as soon as you publish it. That’s why the real work of strategic planning (as boring and slow as it can sound…) is building a shared understanding of what matters - and revisiting it often. If your plan doesn’t adapt, your team will either ignore it or treat it as an empty ritual. And that compounds each time you go through the process. The real return happens when you create a feedback loop between strategy and execution. You articulate a bold direction (one that forces a few clear, meaningful trade-offs), then periodically check if reality still aligns with those guiding ideas. You end up giving your team a lens for deciding which new opportunity or challenge to address fits the plan and which one derails it. And when your OKRs and KPIs present a new finding, you tweak the strategy accordingly, keeping people aligned without throwing out everything you built. Compare that to a typical scenario where the plan is “done", and everyone else chugs along, guessing what leadership wants. It’s no wonder you end up with confusion or half-baked pivots. Instead, if you treat strategic planning as an ongoing narrative (one that people reference in their decision-making), it becomes a scalable, low-touch, high-impact way of making sure all the pieces in your organization are in line with your longer-term goals. So, if you suspect your plan is more of a document than a living guide, ask yourself: does it clarify what you’re not doing? Does it help your team evaluate new ideas without a debate every time? Does it evolve in response to new data? If the answer’s no, you might not need more detail… you might need a clearer set of trade-offs and a process to keep them alive. Connect + DM if you're interested in learning more. Or if you're an operating leader, check out my ebook in my profile on designing a proactive solution for this challenge.

One of our clients pivoted 19 times. 19 TIMES! Today, they’re considered a huge YC success story. The magic happened on pivot #20. A use case 👇 At the time, here’s what wasn’t working: > They sold features, rather than solving a problem. > They had no predictable sales process. Nothing was repeatable > Prospects looked at the tech as a "nice to have" rather than a must-have. > Bc they had 0 processes, they had no feedback loop to test, learn, and iterate. How we turned it around: We broke the problem down from first principles and reasoned up. A framework called RBL (Roots, Branches, Leaves): Roots = Fundamental truths behind purchasing decisions. Branches = Repeatable, tactical processes to execute. Leaves = A continuous feedback loop to optimize and iterate. Here’s how we applied it: 1/ Understanding the ROOTS (why people buy) What are the truths behind purchasing decisions? ✅ People don’t buy products. They buy solutions to problems. ✅ Friction kills action. If it’s too hard to buy, they won’t. ✅ Social proof matters. People trust what others validate. ✅ Pain drives urgency. If the pain of inaction isn’t greater than the effort to change, they ain't buying. ✅ Predictability wins. A repeatable, measurable process moves the needle. 2/ Building BRANCHES (repeatable processes) We built a system that answered key questions: > What’s the real “broken leg” problem the customer is facing? > Can they afford the product? > Who in the org feels the pain most? > Where is the market heading? Next, we removed friction from the sales process: > What’s the “time to wow” when using the product? > How much more efficient is it than alternatives? > What’s the effort required to onboard? > What are the unit economics? Once we had these insights, we designed a repeatable process to: ✅ Convert leads into qualified opportunities. ✅ Run tailored demos that spoke to relevant use cases. ✅ Optimize the funnel for velocity (i.e., paid POCs). ✅ Build case studies and leverage as social proof ✅ Validate success criteria on scopes. ✅ Pull deals across the finish line. 3/ Creating LEAVES (testing & iterating) Once the process was built, we: > Tested the messaging. > Measured deal velocity. > Iterated based on feedback not speculation. Every cycle made the system stronger. This by no means is an isolated incident. It’s pretty common. If your stuck and you don’t know what you don’t know. Here are a few takeaways. ❌ Selling features doesn’t work. Solve real problems. ❌ Repeatability = scalability. Build a structured process. ❌ The fastest way to win is to remove friction. If you enjoy this content you should join 5,000+ founders who get my founder led sales newsletter every Saturday morning, packed with tactical, no-fluff sales strategies you can implement immediately. Link is in the first comment.