Planning Feedback Loops

𝑻𝒉𝒆𝒚 𝒉𝒊𝒕 𝒕𝒉𝒆 𝒈𝒐𝒂𝒍. 𝑪𝒆𝒍𝒆𝒃𝒓𝒂𝒕𝒆𝒅. 𝑴𝒐𝒗𝒆𝒅 𝒐𝒏. 𝑻𝒉𝒆𝒏 𝒓𝒆𝒑𝒆𝒂𝒕𝒆𝒅 𝒕𝒉𝒆 𝒔𝒂𝒎𝒆 𝒎𝒊𝒔𝒕𝒂𝒌𝒆𝒔 𝒐𝒏 𝒕𝒉𝒆 𝒏𝒆𝒙𝒕 𝒑𝒓𝒐𝒋𝒆𝒄𝒕. Sound familiar? A team closed a major deal. Leadership congratulated them. Everyone moved on to the next quarter. No one asked: “What made this work? What would we do differently?” Three months later, they tried to replicate the success — couldn’t. Because no one had captured what actually drove the win. McKinsey found that organizations with structured learning processes are 2.5× more likely to sustain performance, yet most skip the debrief and wonder why progress doesn’t stick. 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘪𝘮𝘱𝘳𝘰𝘷𝘦𝘮𝘦𝘯𝘵 𝘪𝘴𝘯’t 𝘸𝘰𝘳𝘬𝘪𝘯𝘨 𝘩𝘢𝘳𝘥𝘦𝘳 — 𝘪𝘵’𝘴 𝘳𝘦𝘧𝘭𝘦𝘤𝘵𝘪𝘯𝘨 𝘴𝘮𝘢𝘳𝘵𝘦𝘳. 𝑻𝒉𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑳𝒐𝒐𝒑 High-performing teams don’t just execute. They learn, capture, and apply. 1. Execute → Deliver the outcome 2. Reflect → Ask: What worked (and why)? What didn’t (facts, not blame)? What will we do differently next time? 3. Capture → Store lessons where people actually use them (not slides no one opens) 4. Apply → Embed learnings into the next cycle Most teams stop at Step 1. The best close the loop. 𝑻𝒉𝒆 𝑹𝒉𝒚𝒕𝒉𝒎 𝒐𝒇 𝑰𝒎𝒑𝒓𝒐𝒗𝒆𝒎𝒆𝒏𝒕 Improvement isn’t a project. It’s a practice. Daily: 5-min huddles → “What’s working? What’s stuck?” Weekly: 15-min retros → “What did we learn this week?” Quarterly: Strategic debriefs → “What patterns are emerging?” If reflection only happens when things go wrong, you’re learning too late. 𝐂𝐨𝐦𝐦𝐨𝐧 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬 ❌ Celebrating wins without decoding success ❌ Repeating mistakes because no one reflected ❌ Treating improvement as a one-off project ❌ No feedback loops — teams flying blind 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐓𝐞𝐚𝐦𝐬 𝐃𝐨: ✓ Debrief every outcome — success and failure ✓ Make reflection part of weekly rhythm ✓ Capture insights in living systems, not cluttered docs ✓ Apply relentlessly 𝑻𝒉𝒆 𝒉𝒂𝒓𝒅 𝒕𝒓𝒖𝒕𝒉: If you’re not getting better, you’re getting beaten. The fastest teams aren’t the busiest — they’re the most reflective. Reflect: → When did you last debrief a success to understand what made it work? → Do you have a weekly rhythm for learning — or only during crises? 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘪𝘮𝘱𝘳𝘰𝘷𝘦𝘮𝘦𝘯𝘵 𝘪𝘴𝘯’t 𝘢𝘯 𝘦𝘷𝘦𝘯𝘵. 𝘐𝘵’𝘴 𝘢 𝘥𝘪𝘴𝘤𝘪𝘱𝘭𝘪𝘯𝘦. P.S. To build this discipline into your leadership rhythm → 𝑻𝒉𝒆 𝑰𝒏𝒏𝒆𝒓 𝑬𝒅𝒈𝒆 https://lnkd.in/gi-u8ndJ #TheInnerEdge #ContinuousImprovement #ExecutionExcellence #LeadershipRhythm #StrategicLeadership

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.

Treating AI like a chatbot, AKA you ask a question → it gives an answer is only scraching the surface. Underneath, modern AI agents are running continuous feedback loops - constantly perceiving, reasoning, acting, and learning to get smarter with every cycle. Here’s a simple way to visualize what’s really happening 👇 1. Perception Loop – The agent collects data from its environment, filters noise, and builds real-time situational awareness. 2. Reasoning Loop – It processes context, forms logical hypotheses, and decides what needs to be done. 3. Action Loop – It executes those plans using tools, APIs, or other agents, then validates outcomes. 4. Reflection Loop – After every action, it reviews what worked (and what didn’t) to improve future reasoning. 5. Learning Loop – This is where it gets powerful, the model retrains itself based on new knowledge, feedback, and data patterns. 6. Feedback Loop – It uses human and system feedback to refine outputs and improve alignment with goals. 7. Memory Loop – Stores and retrieves both short-term and long-term context to maintain continuity. 8. Collaboration Loop – Multiple agents coordinate, negotiate, and execute tasks together, almost like a digital team. These loops are what make AI agents more human-like while reasoning and self-improveming. Leveraging these loops moves AI systems from “prompt and reply” to “observe, reason, act, reflect, and learn.” #AIAgents

🌀 User journey maps often capture “perfect” journeys users never take. We need to stop designing paths, and start designing loops, especially in AI products ↓ We use journey maps to capture, understand and refine user's experience. However, these maps are merely an idealistic view of what users SHOULD be doing, rather than what they actually ARE doing. Linear paths don't consider detours, circling back and forth, abandonments and returns and shortcuts. In fact, our interactions with reality rarely follow a well-defined, structured script; they’re a series of adjustments and feedback loops — depending on environment, disturbances, decision-making and actions. Workflows shouldn’t be perceived as a rigid cage, but as an orchestrated loop. Matt Fick and Max Peterschmidt suggest to rethink the idea of designing paths and design loops instead, especially with AI products in play. We start with a goal, make decisions, sense what’s going on, study environment, take action and then keep checking again, and again, and again. It follows a simple structure: 🎯 1. Setting a goal First, we establish a goal: what is the user trying to achieve? Desired outcome is the foundation on which the product will ground all its actions and adjustments. We must help people articulate their goal — with slow prompting and better calibration (knobs, pre-prompts, buttons, sliders). 🌡️ 2. Studying the current state (Sensors, Environment) To improve something, we must understand its current state. We find the right sources and collect the right inputs to get a snapshot of the current state. Often there are many meaningful inputs, and often they are very difficult to predict ahead of time. 🧠 3. Making decisions (Controller) Next, we evaluate the data and compare it against the goal. We come up with meaningful actions and get recommendations, grounded in trusted sources. Mapping the reasons for recommendations is critical for building trust and confidence — with AI, but not necessarily with LLMs. 🚀 4. Taking actions (Actuator) Once we decide that an adjustment is necessary, we take an action, or we ask agents to take an action — directly manipulating the environment closer to the desired outcome. The actions are typically initiated or approved by humans, and that’s what we mean with “human in the loop”. 🧲 5. Studying and refining the new state We gather data about the changed environment, and then use these inputs to suggest the next batch of changes as output. With nested loops, when many people or AI agents are involved, output in one loop becomes an input in another and informs next decisions and actions there. An interesting and realistic model in AI world, matching the complexities of the real world better than journey maps often do. Indeed, workflows aren’t rigid cages — they are non-linear, cyclic and must be highly adaptive to be meaningful. They must sense, respond and learn — and loops do just that.

Closing the Loop Between Planning and People Most planning starts with good intentions. Too much of it ends as a document the neighborhood never feels. We’ve all seen it: a glossy plan, a community meeting, a final report. Then the block stays the same. Sidewalk gaps. Vacant lots. “Coming soon” signs that never come. That’s the gap I keep coming back to. Not a gap in ideas. A gap in connection. Cities plan because they have to: growth, housing, infrastructure, climate risk. Communities show up because they care and because they know things no spreadsheet can capture. So why do we still end up with plans that don’t reach the people they’re supposed to serve? Because engagement gets treated like an event instead of a feedback loop. Implementation gets treated like “later” instead of the whole point. And planning stops at permission. Policy creates permission. Delivery creates belief. Here’s the question: What would change if we measured planning success by what residents can actually see, touch, and use? A few moves that close the loop: -Write a “Block Version” of the plan. Plain language: what’s changing, when, who owns the next step, and where the money comes from. If people can’t understand it, they can’t hold anyone accountable. -Put execution next to vision. Every major recommendation needs an owner, a timeline, a funding path, and a first 90-day action. This is how plans stop becoming shelf documents. -Build a standing feedback rhythm. Quarterly check-ins. Resident advisory groups with stipends. Public updates that track what got done and what didn’t. Trust doesn’t survive silence. -Fund the people work. Translation, childcare, stipends, door knocking, relationship-building. We budget for reports, then act surprised when the plan doesn’t land. Community trust is infrastructure too. -Deliver one proof project. A safer crossing. A small storefront rehab. A pop-up third place. A small-scale housing pilot. Something neighbors can point to and say, “That came from the plan.” Belief through delivery. This is also where r.plan fits. We help connect the dots between city planning, community vision, and real projects on the ground by pairing analysis with lived experience and strategy with implementation. Clear owners. Clear sequencing. Clear accountability. Not just what we build, but how we build. Your turn: Where have you seen planning lose the thread between the document and the block, and what’s one step your city could take this year to close that loop?

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.

When the Head of Product drives strategy top-down, PMs get frustrated. But when PMs drive bottom-up planning...execs get nervous. And when they don’t talk? Roadmaps fall apart. The best product planning lives in the middle. You need top down planning and bottom-up discovery Too often, orgs pick just one side: 🧠 Top-down: Execs set bold bets. PMs execute — even when the data says “this won’t land.” 👟 Bottom-up: PMs chase user needs. Strategy gets lost in the backlog. Here’s what works: strategy as a loop, not a broadcast. 1️⃣ Set Strategic Guardrails Top-down strategy should provide the North Star. Not a list of features. But a set of outcomes: → What problems are we trying to solve at the business level? → What does success look like 12–24 months out? Think: revenue targets, market positioning, platform investments. PMs need these boundaries to prioritize with purpose. 2️⃣ Run Bottom-Up Discovery This is how we understand customer value. → Who is the core customer? → Where's the true pain point? → What patterns are emerging across segments? Not just voice-of-customer — real behavior, real usage. PMs should synthesize signal, not just collect noise. 3️⃣ Drive the Planning Loop Now comes the hard part: translation. → Which bottom-up signals align with strategic goals? → Where do they challenge the current direction? This is where planning becomes strategic. You’re not just slotting features into a timeline — you’re shaping the roadmap based on live feedback. Push for course-correction before commitments solidify. 4️⃣ Package for Executive Buy-In Insights only drive action when they’re communicated in the right language. → Use exec framing: risk, revenue, roadmap. → Use BLUF and the 5-slide rule. → Show tradeoffs, not just problems. This is where influence happens — not just up, but across product, design, eng, marketing. Final thought: The best strategy lives at the intersection of business value and customer value. Not just vision. Not just feedback. Real planning that connects the two. -- 👋 I’m Ron Yang, a product leader and advisor. Follow me for insights on product leadership & strategy.

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.

I heard a familiar line on a customer call last quarter: “We hit plan, but the business isn’t where it should be.” Here’s what happened: Their revenue number looked great on paper. But underneath, territories were unbalanced, two top reps had burned out, and incentives were pushing deals that didn’t match the company’s new strategy. The plan wasn’t bad…it was just frozen in time. Built in January. Irrelevant by June. We’ve all done it. You lock your sales plan for the year, then spend the next six months trying to defend it instead of improve it. Here’s the truth: in today’s market, annual planning is a luxury. - The best teams plan in motion. - They review capacity monthly. - They adjust quotas when hiring slips. - They rebalance territories when market demand shifts. - And they treat compensation like behavior design, not math. Companies that review incentives quarterly see 3× higher growth than those who do it once a year. Not because they work harder but because they work with reality. Agility isn’t about moving fast. It’s about removing friction between what’s happening and what you do about it. So next time you’re tempted to “wait until Q4 to fix it,” ask yourself: Would you rather defend the plan, or outperform it? How often does your team revisit quotas, territories, or incentives and what’s stopping you from shortening that loop?

How do you actually know if your learning and development program is working? It’s a question that comes up all the time for AEC firms. There’s no shortage of ideas when it comes to training. New tools, new frameworks, new skills to build. The challenge isn’t generating ideas. It’s knowing which ones truly matter. In this clip, what stands out to me is a simple but powerful approach: let the project work itself tell you where to invest in L&D. At BWBR, their Landmark Learning program for emerging professionals is shaped directly by feedback from the people closest to the work—quality assurance and construction administration teams. These groups see, in real time, where projects succeed and where they struggle. They assign grades across key categories—like how well teams are handing off projects—and look for patterns. If project teams are consistently underperforming in a certain area, that becomes a priority for training. That same system creates a feedback loop. The issues identified by QA and CA teams inform the training agenda. Then over time, those same metrics show whether performance is actually improving. You’re not guessing if the training worked because you can see it in the data, in the field, in the way projects are delivered. And that’s where learning and development starts to feel less like a set of programs and more like a system for continuously improving how a firm operates. Because the goal of learning and development isn’t to deliver training. It’s to improve the performance of individuals, of project teams, and of the firm as a whole. When you design a feedback loop like this in your L&D efforts, prioritization becomes clearer. Investment becomes more intentional. And learning becomes directly connected to outcomes. This clip comes from “Redesigning Learning for the Next Generation of AEC Talent | Dan Hottinger and Kari Shonblom of BWBR”, episode 1 of the Smarter by Design podcast. 📺 🎧 Watch or listen to the full episode here: https://lnkd.in/gevBva5y #AEC #KnowledgeManagement #ModernLearningOrganizations #SmarterByDesign