Learning From Failures In Innovation Scaling

Most companies claim they embrace failure. But walk into their Monday meetings, and watch people scramble to hide their missteps. I've seen it countless times. The same leaders who preach 'fail fast' are the first to demand explanations for every setback. Here's the uncomfortable truth:  Innovation dies in environments where people feel safer playing it safe. But there's a difference between reckless failure and strategic experimentation. Let me show you exactly how to build a culture that genuinely embraces productive failure: 𝐂𝐡𝐚𝐧𝐠𝐞 𝐲𝐨𝐮𝐫 𝐩𝐨𝐬𝐭-𝐦𝐨𝐫𝐭𝐞𝐦 𝐦𝐞𝐞𝐭𝐢𝐧𝐠𝐬 Stop asking "Who's fault was this?" and start asking: "𝘞𝘩𝘢𝘵 𝘩𝘺𝘱𝘰𝘵𝘩𝘦𝘴𝘪𝘴 𝘸𝘦𝘳𝘦 𝘸𝘦 𝘵𝘦𝘴𝘵𝘪𝘯𝘨?" "𝘞𝘩𝘢𝘵 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤 𝘥𝘢𝘵𝘢 𝘥𝘪𝘥 𝘵𝘩𝘪𝘴 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘨𝘪𝘷𝘦 𝘶𝘴?" "𝘏𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘶𝘴𝘦 𝘵𝘩𝘪𝘴 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘰𝘶𝘳 𝘯𝘦𝘹𝘵 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯?" 𝐂𝐫𝐞𝐚𝐭𝐞 '𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐬𝐡𝐨𝐰𝐜𝐚𝐬𝐞𝐬' Monthly meetings where teams present their failed experiments and the insights gained. The key? Leaders must go first. Share your own failures openly, specifically, and without sugar-coating. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐭𝐡𝐞 "24-𝐡𝐨𝐮𝐫 𝐫𝐮𝐥𝐞" After any setback, give teams 24 hours to vent/process. Then require them to present three specific learnings and two potential next steps. This transforms failure from a dead end into a data point. Most "innovative" teams are just risk-averse businesses in disguise. They've mastered innovation theater, not actual innovation. Don't let your people think they need permission to innovate. Instead, start building systems and a culture that make innovation inevitable.

After selling Ampush (200+ employees, $30M revenue), I thought I knew how to spot winning businesses. Then I launched 6 startups in 2 years: • 2 failed completely • 1 barely survived • 2 hit 8 figures That experience forced me to rethink everything I believed about business. Here’s how one brutal realization changed my approach forever. ↓ Let me tell you about two businesses I started during that time: 1. Kahani: On paper, it was a cutting-edge idea. We built a tool to make e-commerce websites feel like TikTok or Instagram. Slick, innovative, and totally “of the moment.” But adoption was painfully slow. Customers weren’t lining up to use it. Worse, the few that tried it didn’t stick around. Despite countless pivots and a lot of hope, Kahani ultimately shut down. 2. GrowthAssistant: In stark contrast, GrowthAssistant offers offshore marketing talent to overburdened teams. No fancy tech, no groundbreaking innovation. A straightforward solution to a problem people were desperate to solve. Within months, it was thriving. Today, it’s an eight-figure business growing rapidly. The turning point: We took both Kahani and Growth Assistant to Shoptalk, a massive trade show for retail and e-commerce. Each company sent one salesperson. When the dust settled, the numbers didn’t lie: • Growth Assistant secured 25 meetings • Kahani got... 5 Sitting there with the data in my hands, I couldn’t lie to myself anymore. Cool ideas don’t matter unless they solve urgent, painful problems. I had been building ideas I wanted to work on, not ideas the market demanded. I had to make a big change. Here’s the framework I built from that failure: 1. Start with demand Before building anything, we now test the market. For GrowthAssistant, I sent emails to my network pitching the concept. When I got immediate interest (and even pre-sales), I knew we had something. 2. Fail fast, intentionally If something’s going to flop, I want to know as quickly and cheaply as possible. We launch small bets, give them a timeline, and shut them down ruthlessly if they don’t gain traction. 3. Be brutally honest At Shoptalk, I could’ve blamed the market, timing, or execution for Kahani’s lack of success. But deep down, I knew the problem was that we hadn’t validated demand. Accepting that lets us double down on winners like GrowthAssistant. Failure is inevitable, but it’s also the best teacher. That’s why I believe every founder needs to develop a sharp instinct for identifying product-market fit. And have the courage to admit when it isn’t there. The market will tell you the truth, you just have to be willing to listen.

Your "best practices" might be killing your improvement efforts. Here's why: During WWII, military analysts studied planes returning from combat to see where to add armor. Most bullet holes were on the wings and fuselage. So they wanted to reinforce those areas. Then statistician Abraham Wald said: "You're looking at the wrong data." The planes you're studying SURVIVED. The ones shot in the engine and cockpit? They never made it back. This is survivorship bias. And it's destroying your continuous improvement efforts. Here's how: - You study your "successful" processes - You benchmark against top performers only - You ignore the failed experiments - You copy what worked elsewhere But you're missing the critical data: → Why did some improvement initiatives fail? → What problems aren't being reported? → Which "best practices" actually caused failures? → What are the unsuccessful companies doing wrong? The manufacturing reality: For every process improvement that worked, 3 didn't make it to implementation. But we only study the survivors. Better approach: - Document failed experiments and why they failed - Study processes that broke down under pressure - Interview people who left your company - Analyze near-misses, not just successes - Look at what your struggling competitors are doing The real insights aren't in your success stories. They're in your failures. What failed improvement initiative taught you the most? Share it below - let's learn from the data we usually ignore.

Ever wondered what a failed project can teach you? Here’s the truth: 𝑭𝒂𝒊𝒍𝒖𝒓𝒆 𝒊𝒔 𝒐𝒇𝒕𝒆𝒏 𝒕𝒉𝒆 𝒃𝒆𝒔𝒕 𝒕𝒆𝒂𝒄𝒉𝒆𝒓! 5 Lessons from a Failed Geospatial Project! After facing my fair share of setbacks in geospatial projects, I’ve learned that each failure holds a lesson that reshapes how we approach future work at New Light Technologies Here’s what I learned—and how it’s transformed my entire process: 1️⃣ 𝘾𝙡𝙖𝙧𝙞𝙩𝙮 𝙊𝙫𝙚𝙧 𝘾𝙤𝙢𝙥𝙡𝙚𝙭𝙞𝙩𝙮 What Went Wrong: We tried to solve everything, and in the end, we solved nothing. Focus on one clear goal at a time. Simplify the problem, and progress will follow. 2️⃣ 𝘿𝙖𝙩𝙖 𝙌𝙪𝙖𝙡𝙞𝙩𝙮 = 𝙋𝙧𝙤𝙟𝙚𝙘𝙩 𝙌𝙪𝙖𝙡𝙞𝙩𝙮 What Went Wrong: Poor data quality led to outputs no one trusted.  The Fix: Invest in data validation—quality is always more important than quantity. 3️⃣ 𝙎𝙩𝙖𝙠𝙚𝙝𝙤𝙡𝙙𝙚𝙧 𝘾𝙤𝙢𝙢𝙪𝙣𝙞𝙘𝙖𝙩𝙞𝙤𝙣 𝙞𝙨 𝙆𝙚𝙮 What Went Wrong: Miscommunication caused misaligned expectations across teams.  The Fix: Regular, open communication keeps everyone aligned and on track. 4️⃣ 𝙋𝙡𝙖𝙣 𝙛𝙤𝙧 𝙎𝙘𝙖𝙡𝙖𝙗𝙞𝙡𝙞𝙩𝙮 What Went Wrong: The system couldn’t scale, leaving users frustrated.  The Fix: Design with growth in mind. Ensure systems are built to adapt. 5️⃣ 𝘼𝙘𝙩𝙞𝙤𝙣𝙖𝙗𝙡𝙚 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨 > 𝙋𝙧𝙚𝙩𝙩𝙮 𝙈𝙖𝙥𝙨 What Went Wrong: Beautiful maps that didn’t help drive decisions.  The Fix: Focus on actionable insights. Results speak louder than aesthetics. Failure isn’t the end—it’s the beginning of a new approach. Ready to turn your data into actionable insights? Let’s collaborate at newlighttechnologies.com to bring your next project to life. Follow Ghermay A. #Geospatial #Lessons #innovation #DataScience #ProjectManagement

Scaling kills more great ideas than failure ever could. Here’s how to prevent yours. Scaling should feel like winning. But most great ideas don’t just stumble as they grow, they collapse. I learned this the hard way: Scaling was supposed to be our big win. Instead, it became our undoing. Costs spiraled, systems broke, and the team burned out. Six months later, we shut everything down. Looking back, the mistakes were obvious. But only after it was too late. Scaling isn’t about doing more. It’s about designing for growth. Here are the 7 traps that kill ideas when they scale—and how to avoid them. 1. False Positives Early wins are seductive. They blind you to hidden risks and fuel overconfidence. ✅ Fix It: Test your idea in multiple scenarios and push for independent validation. 2. Biased Testing Testing the wrong audience is like reading the wrong map. It’s a straight path to failure. ✅ Fix It: Make sure your test group reflects your actual customer base. 3. Unscalable Elements Scaling isn’t about cloning your stars. It’s about creating a system that shines without them. ✅ Fix It: Build scalable systems that work without relying on individuals. 4. Negative Spillovers Scaling isn’t just about growing bigger. It’s about avoiding the cracks that widen as you expand. ✅ Fix It: Plan for unintended consequences and design systems that scale smoothly. 5. Cost Overload Scaling too fast is like pouring gasoline on a fire. It burns bright but won’t last. ✅ Fix It: Plan for economies of scale and focus on cost efficiency. 6. Human Limitations Success tied to irreplaceable people isn’t success. It’s a ticking time bomb. ✅ Fix It: Create training programs and processes that deliver consistent results. 7. Lack of Stress Testing Scaling is where weaknesses stop hiding. Stress-test early—or watch them break everything. ✅ Fix It: Stress-test your idea under diverse, real-world conditions to uncover hidden flaws. Fix these traps early, and your growth won’t just be bigger. It’ll be unstoppable. What’s one scaling trap you’ve seen most? ♻️ Share this to help someone scaling their big idea. Follow Amy for more business strategies like this.

Incidents in modern software-driven businesses present opportunities that are much greater than is typically recognized. Developing effective countermeasures, preventative designs, and other improvements require understanding an incident in deeper ways than simply when it occurred, how long it lasted, and what the customer impact was. This deeper understanding comes from reconstructing the event(s) in the context they were in, exploring the multiple perspectives of those who are responsible for responding to the incident and those defending the organization from these types of events, and connecting them in ways that have lasting influence on the business. “Learning from failure” does not come from filling out a template and plotting shallow data on a chart. It means developing and maintaining the expertise to know how to analyze incidents deeply, identify which elements of the event are most valuable and which are not, and synthesizing this in ways that provide the greatest insight for the broadest audience possible. Most importantly, it means doing this analyses efficiently, because businesses cannot wait for a dissertation to be published every time an outage happens. This is what we train people to do.