Shifting to Results-Driven Engineering Practices

Why product roadmaps should be outcome based not feature-driven We do sprints to ship features, and they don’t always work out. Why? Because features alone don’t move the needle -outcomes do. A practice that I usually follow is to ask myself: What problem are we solving, and how will we measure success?” And that’s how we pivot from feature factories to outcome-driven roadmaps with actionable steps to make it stick. 𝗪𝗵𝘆 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 > 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Outcome-based roadmaps focus on measurable results (e.g., “Increase free-to-paid conversion by 15%” vs. “Build a pricing calculator”). This shift: - Aligns teams around business goals, not just deliverables. - Empowers creativity (solve the problem, don’t just check a box). - Reduces waste by killing initiatives that don’t drive impact. But how do you actually make this work? Here’s My Practical Playbook 👇🏻 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 “𝗪𝗵𝘆” - Define outcomes tied to business goals: Partner with leadership to align on 1-2 KPIs per quarter (e.g., “Reduce churn by 10%”). - Ask this question: “If we deliver X feature, what outcome does it enable?”. If there’s no clear answer, rethink it. 2️⃣ 𝗕𝗿𝗲𝗮𝗸 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗶𝗻𝘁𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 Outcomes are broad—break them into testable hypotheses. - Example: To “Increase user engagement by 20%,” run:   - A/B test push notification timing.   - Pilot a gamified onboarding flow.   - Measure DAU/WAU ratios weekly. 3️⃣ 𝗔𝗱𝗼𝗽𝘁 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 - OKRs: Link Objectives (outcomes) to Key Results (metrics). - Impact Mapping: Visualize how features connect to goals. - RICE Scoring: Prioritize initiatives by Reach, Impact, Confidence, Effort. 4️⃣ 𝗚𝗲𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗕𝘂𝘆-𝗜𝗻 - Frame outcomes as ROI: Show how “Reduce support tickets by 25%” cuts costs. - Prototype outcomes first: Share a mock roadmap with leadership, highlighting gaps in current feature-centric plans. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Track leading indicators (e.g., user behavior changes) alongside lagging metrics (e.g., revenue). - Celebrate “failures”: Killing a feature that didn’t drive outcomes is a win. 𝟯 𝗧𝗵𝗶𝗻𝗴𝘀 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 - - Vague outcomes: “Improve UX” → ❌ | “Reduce checkout abandonment by 20%” → ✅. - Overloading the roadmap: Focus on 1-2 outcomes per quarter. - Ignoring feedback loops: Revisit outcomes bi-weekly—adapt as data comes in. This week, try this: Audit your roadmap. For every feature, ask: “What outcome does this serve?” If it’s unclear, reframe it, or cut it. I believe outcome-based roadmaps is a survival tactic. Let’s build products that matter. 👉 How are you bridging the gap between features and impact? Would love to know your process.

AI‑First Software Engineering is a mindset shift — not a new tool. Tools amplify how we work; mindset rewires why we build. The shift looks like: From outputs → outcomes: Ship features that move KPIs, not just code. From prompts → practices: Standardize patterns, reviews, and guardrails; make “how we use AI” part of engineering hygiene. From speed → speed with safety: Automate tests, policy checks, and traceability by default. From heroes → systems: Knowledge bases, reusable contexts, and feedback loops. From projects → platforms: Treat models, data, and pipelines as productized assets with lifecycle ownership. From adoption → accountability: Teams own value delivered, risks managed, and learning captured. Start now: Make value visible (lead time, defect escape, developer experience). Codify governance in CI/CD (security, privacy, compliance). Manage knowledge as code (prompts, contexts, patterns). Close the loop (telemetry → insights → improvements, weekly). AI won’t replace engineers — engineers who master AI will outpace those who don’t. Ready to rewire your Software Engineering Process? #UST #AIFirst #SDLC #SoftwareEngineering #GenAI #DevOps #MLOps #ProductEngineering #EngineeringExcellence

Over the years, I’ve seen a recurring challenge in execution—teams often start with what they’re building instead of why it matters. A feature gets shipped. A project is completed. But then comes the real question—Did it drive the intended business impact? One approach that has significantly improved execution and alignment is right-to-left planning—starting with the desired business outcome and working backward to determine the most effective way to achieve it. I’ve seen this shift help teams focus on impact rather than just deliverables, leading to better prioritization and clearer decision-making. Instead of asking: ❌ “What do we need to build this quarter?” A more impactful approach is to ask: ✅ “What business outcome are we driving, and what’s the simplest way to achieve it?” For example: Rather than saying, “Let’s build Feature X,” the focus shifts to, “We need to increase enterprise adoption by 30%—what’s the most efficient way to get there?” Why This Approach Works * Ensures alignment with business goals—teams don’t just execute; they execute with purpose. * Prevents scope creep—by focusing on the minimum effective work needed to achieve impact. * Enables better prioritization—decisions are guided by impact, not just effort. * Brings product, engineering, and business teams together—everyone speaks the same language: outcomes. This shift in thinking has made a tangible difference—teams become more aligned, execution is sharper, and decisions are driven by impact rather than just effort. Curious to hear—what’s been your experience? Would love to know and learn from others!

For decades, engineering teams have been measured by lines of code, commit counts, and PRs merged—but does more code actually mean more productivity? 🚀 Some of the best developers write LESS code, not more. 🚀 The fastest-moving teams focus on outcomes, not just output. 🚀 High commit counts can mean inefficiency, not impact. Recent research from DORA, GitHub, and real-world case studies from IT Revolution debunk the myth that developer activity = developer productivity. Here’s why: 🔹 DORA Research: After studying thousands of engineering teams, DORA (DevOps Research & Assessment) found that the best teams optimize for four key engineering performance metrics: ✅ Deployment Frequency → How often do we ship value to users? ✅ Lead Time for Changes → How fast can an idea go from code to production? ✅ Change Failure Rate → Are we improving quality, or just shipping fast? ✅ MTTR (Mean Time to Restore) → Can we recover quickly when things go wrong? → Notice what’s missing? Not a single metric is based on lines of code, commits, or individual developer output. 🔹 GitHub’s Data: GitHub found that developers working remotely during 2020 pushed more code than ever—but many felt less productive. Why? Longer workdays masked inefficiencies. More commits ≠ meaningful work; some were just fighting bad tooling or slow reviews. Teams that automated workflows (CI/CD, code reviews) merged PRs faster and felt more productive. 🔹 IT Revolution case studies: High-performing engineering orgs measure outcomes, not just outputs. The best teams: Shift from tracking commit counts → to measuring customer value. Use DORA metrics to improve DevOps flow, not micromanage engineers. View engineering productivity as a team effort, not an individual scoreboard. If you want a high-performing engineering org, don’t just push developers to write more code. Instead, ask: ✅ Are we shipping value faster? ✅ Are we reducing friction in our workflows? ✅ Are our developers able to focus on meaningful work? 🚨 The takeaway? Great engineering teams don’t write the most code—they deliver the most impact. 📢 What’s the worst “productivity metric” you’ve ever seen? Drop a comment below 👇 #DeveloperProductivity #SoftwareDevelopment #DORA #GitHub #EngineeringLeadership

Motion does not always equal progress. This is especially true when a team is executing well on a process designed for a problem that no longer exists. High performing teams challenge processes often, ensuring that they are fit for purpose and connected to the results needed now. Here are five practices to ensure your team successfully shifts from process focused to outcome focused:  𝗔𝘀𝗸 "𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘄𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝗮𝗰𝗵𝗶𝗲𝘃𝗲?" Doing this ahead of regular meetings or reoccurring tasks allows for a simple audit of process-creep. Interrogating the routine keeps you focused on outcomes. If the question can’t be answered easily, the meeting or task has likely outlived its purpose. By making a "process census" a regular habit, each recurring activity gets a fresh justification or a graceful exit. 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝘁𝗵𝗲 𝗺𝗮𝗽 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗱𝗲𝘀𝘁𝗶𝗻𝗮𝘁𝗶𝗼𝗻. Process is a map. Useful, but not the point. The risk is that people start treating the map as sacred, even when the terrain has changed. When launching any initiative, write down the desired outcome first, in plain language, before any process discussion begins. This forces the team to design processes in service of the result, not the other way around. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲 "𝗺𝗶𝗻𝗶𝗺𝘂𝗺 𝘃𝗶𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀." Borrow from the startup world. Ask: what is the least amount of process needed to reliably reach this outcome? This isn't about cutting corners. It's about exposing all the steps that exist because "we've always done it this way" rather than because they move the needle. Have your team map a current workflow and challenge every step with: does this directly contribute to the result, or does it just feel like progress? 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝗶𝗲𝘀. When teams track tasks and milestones rather than results, they get very good at being busy. Shift the scorecard. Replace activity-based status updates ("we completed five key account reviews") with outcome-based ones ("customer response time dropped 12%"). What gets measured shapes what people focus on. 𝗜𝗻𝘃𝗶𝘁𝗲 "𝗳𝗮𝘀𝘁 𝗽𝗮𝘁𝗵" 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀. In many cultures, suggesting a workaround feels like challenging the institution- so people silently comply with inefficient process. Leaders can change this by routinely asking: "Is there a faster way to get to the same result?" That question, asked openly and without judgment, signals that agility is valued and that process is a tool, not a rule. The ever-increasing pace of change requires leaders to ensure that process is serving its purpose and no more. Good process deserves respect. It creates consistency, reduces errors, and improves efficiency. This issue isn’t process itself, it’s a culture that is afraid to challenge process. Healthy challenge about how best to do the work, keeps the focus on outcomes. 

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.

We used to build products like an assembly line. Recently, I shared how we shifted from an "output" mindset to an "outcome" mindset at Avoma. That was just the surface. Underneath, something deeper was happening – a rewiring of how our teams think, behave, and collaborate. These shifts weren’t just tactical. They were cultural. Here are 4 mindset shifts we’ve made that changed how we build – and more importantly, why we build what we do. 𝟭. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 > 𝗢𝘂𝘁𝗽𝘂𝘁 This one’s foundational. It’s not about completing tasks or shipping features; it’s about delivering an outcome and making an impact. Instead of “What did we launch?”, the question becomes “Did it help the customer win more deals?”, “Did it save them time?”, etc. Engineers need to ask, “How will this change what the customer does?” instead of “When do we go live?” 𝟮. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 (𝗳𝗼𝗿 𝗿𝗲𝗮𝗹 𝘁𝗵𝗶𝘀 𝘁𝗶𝗺𝗲) Everyone says they’re customer-centric. But the "centric" part means the customer is at the center – not your design system, data model, old architecture, or resource constraints. Too often, we prioritize “what’s easier for us” over “what’s better for them.” The hard truth? The customer doesn’t care about your tech stack. They care about their problem being solved. We started making product decisions with the customer in the room (figuratively), asking: What would they choose if they were sitting here with us? 𝟯. 𝗖𝗿𝗼𝘀𝘀-𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝘁𝗮𝗿𝘁 A lot of teams say they’re agile, but still operate like waterfall in disguise. PM does the research → design mocks up solutions → eng builds it → QA tests. It’s linear. It’s slow. It’s siloed. Now, we bring product, design, and engineering together at the start – before there’s even a solution. Everyone has a seat at the table when we’re 𝘀𝘁𝗶𝗹𝗹 𝗷𝘂𝘀𝘁 𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. And the magic? Engineers come up with product ideas. Designers push technical boundaries. PMs get challenged on assumptions. 𝟰. 𝗩𝗶𝘀𝘂𝗮𝗹 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝘃𝗲𝗿 𝘄𝗮𝗹𝗹𝘀 𝗼𝗳 𝘁𝗲𝘅𝘁 Slack threads. Long PRDs. Endless documentation. That’s how you 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 decisions. Not how you 𝗺𝗮𝗸𝗲 them. With Vibe coding on the rise, we started pushing for visual thinking early – rough sketches, clickable prototypes – anything that brings ideas to life. When you see a customer journey, it’s easier to feel where the friction is. When you mock something quickly, others can riff on it. It’s messy, but that mess is where clarity emerges. The truth is, changing how we build wasn’t just a process update. It was a mindset evolution. And the biggest unlock? We stopped trying to be "𝗿𝗶𝗴𝗵𝘁 𝘂𝗽𝗳𝗿𝗼𝗻𝘁" and started trying to get it "𝗿𝗶𝗴𝗵𝘁 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿".

Are you just shipping features, or are you driving real business impact? It’s easy to fall into the feature factory trap. For many teams, shipping new features equals progress. But the truth is that it often leaves them spinning their wheels without real momentum. When we focus only on release counts, we miss the bigger picture: solving actual user problems and pushing business goals forward. Success in product isn’t about how much you deliver. It’s about what changes because of what you delivered. That’s why product managers should lead teams to ask "why" at every step, making sure their work aligns with strategic goals. This shift from output to outcomes is crucial for escaping the build trap and creating meaningful impact. To break the cycle, product teams need to lead with strategy: ✅Ask why before building ✅Align work to real outcomes ✅Use feedback to iterate ✅Connect product decisions to business results That’s how you escape the build trap and build things that actually matter. So, what’s driving your roadmap right now: output or outcomes? Let me know in the comments.

The best systems need the least management. Yet we keep adding steps, checkpoints, and approvals. I used to believe great companies were built on comprehensive processes. My first startup had detailed procedures for everything — each sales interaction, support ticket, and feature release followed a precise playbook. As we scaled, our process documentation grew faster than our revenue. Team velocity slowed. Innovation suffered. Talented people spent more time following protocols than solving problems. The turning point came when we rebuilt our approach around outcomes instead of activities: 1️⃣ We replaced activity metrics ("number of calls made") with outcome metrics ("deals progressed") 2️⃣ We stopped documenting how tasks should be done and started defining what success looked like 3️⃣ We built automated guardrails instead of manual checkpoints 4️⃣ We focused quality control on system inputs and outputs, not every step in between The results were transformative. Teams moved faster. Quality improved. People stayed energized. Business process exists to manage risk and ensure quality—both valid concerns. But most companies implement these controls at the tactical level when they belong at the systems level. Think of it like this: You can micromanage a road trip by dictating every turn, or you can set a destination, provide a reliable vehicle with good brakes, and trust the driver to navigate. The difference is critical. Tactical processes control behaviors while systems-level thinking shapes environments. Some practical shifts to consider: 1️⃣ Replace decision chains with clear boundaries and after-action reviews 2️⃣ Substitute detailed instructions with clear success criteria 3️⃣ Trade activity monitoring for outcome measurement 4️⃣ Swap manual checks for automated testing 5️⃣ Replace rigid workflows with principles and guardrails Design systems that make quality inevitable, not processes that make errors impossible. Operational excellence is fundamentally about outcome clarity, not process quantity. #startups #founders #growth #ai