Why Innovation Needs Structured Problem-Solving

🚫 Stop wasting millions on innovation. I’ve seen too many corporate innovations fail — not because of a lack of effort or brilliant minds. The real problem? Companies rush to build and push new products. They chase perceived problems. Leadership spots a trend — maybe it’s AI, maybe it’s a competitor’s new feature — and the directive follows: 👉 “Go build that!” But here’s the truth: 💡 We’re addicted to solution. We build solutions in search of a problem. We get excited by the what, but we don’t deeply understand the why. Ask yourself: - How many internal tools just sit unused? - How many features launched that solved no real pain? - How much tech was bought without first understanding the core business challenge? 👉 The most impactful innovation doesn’t start with a product idea or technology. It starts with deep understanding of the problem space. And that’s hard, uncomfortable work. It means: 🔹 Understanding human behavior — seeing how people struggle and adapt. 🔹 Challenging assumptions — asking why things are done this way instead of accepting the status quo. ✅ Can you clearly state the problem? ✅ Is it validated with evidence? ✅ Does it impact real people? If not, your solution is a gamble — a shot in the dark that wastes time, money, and energy. Let’s change corporate innovation culture. Before you approve the next solution: 👉 Challenge your teams. 👉 Make them articulate the problem clearly. 👉 Demand evidence. A problem-first approach isn’t slower. It’s smarter and more impactful. #Innovation #CorporateInnovation #ProblemSolving #DesignThinking #ProductDevelopment #IgnoredTruths #BusinessTransformation

𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼𝗺𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗮𝗺𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘀𝘁𝗮𝗹𝗹—𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗷𝘂𝘀𝘁 𝗮𝘀 𝗵𝗮𝗿𝗱? In research conducted with Johnathan Cromwell, Kevin J. Johnson, and Amy Edmondson, we studied more than 160 innovation teams—including those in a Fortune Global 500 company—and found that it's not just how much teams learn that matters, but when and how they learn. We identified four core modes of team learning: 𝗥𝗲𝗳𝗹𝗲𝘅𝗶𝘃𝗲 — assessing goals, roles, and strategies 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 — brainstorming, prototyping, testing new ideas 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 — scanning the environment for trends, signals, and shifts 𝗩𝗶𝗰𝗮𝗿𝗶𝗼𝘂𝘀 — drawing lessons from others who’ve done similar work The most effective teams didn’t try to do everything at once. They began and ended with reflexive learning, anchoring their work in shared understanding. They placed exploratory learning (experimental and contextual) in the middle. This rhythm—reflection → exploration → reflection—helped them reduce friction, integrate insights, and build real momentum. We also found that vicarious learning can be combined with reflexive learning in the same project phase with positive results. But when teams mixed reflexive with experimental or contextual learning in the same phase, performance suffered. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Innovation doesn’t thrive on more learning. It thrives on structured learning. Teams that sequence and separate their learning activities make faster, clearer progress. We’ve summarized the findings from our research, published in Administrative Science Quarterly—a leading journal in organizational research—in this new Harvard Business Review article. Link in comments.

Innovation is not a random flash of brilliance. It is not dependent on luck. It's a business process that can be measured, managed, and optimized just like any other. Yet all too often product leaders cling to the outdated notion that innovation is more art than science. They fear that imposing process will somehow suffocate the creative spirit. But here's the hard truth: if you believe that innovation is more about luck than process, you're consigning your company to a future of aimless guesswork and 70%+ failure rates. Successful innovation requires embracing it as a discipline. How do you make the shift? Start by educating your teams on proven innovation methods like Outcome-Driven Innovation (ODI), a process that puts Jobs-to-be-Done Theory into practice. This approach provide a structured framework for uncovering unmet customer needs, formulating strategy, and systematically devising winning solutions. But be warned: deeply ingrained mindsets don't change overnight. It takes persistent evangelism from leaders like you to help the organization unlearn old ways and master the true craft of innovation. The payoff is worth it. By making innovation a core competency, you can replace guesswork with repeatable success. Luck is a fickle friend - process is a reliable partner.

Most innovation efforts don’t fail because of execution. They fail because the wrong problem is being solved. I recently joined Vasco Duarte on the Scrum Master Toolbox Podcast, and the conversation brought me back to one of my earliest lessons in innovation. Sitting in a diner booth with my dad, stopwatch in hand, learning to observe what was actually happening before jumping to solutions. That lesson has stayed with me for decades. In this episode, we unpacked three patterns I continue to see across organizations: 1. 98% of teams fall in love with their solution before validating the problem. At IBM Watson Health, I often had to pull engineering back from building in isolation. A reference customer is an N of 1, it doesn’t prove real demand. 2. Google’s 70 / 20 / 10 model exists (and works) for a reason. 70% core. 20% adjacencies. 10% net new ideas. That 10% bet drives a disproportionate share of future growth. Skip it, and you stay stuck in incremental gains. 3. If your problem isn’t clear, your experiment isn’t either. No clarity → wrong MVP → misleading results. Good experimentation starts with a problem that is clear, measurable, significant, and actionable. We also discussed a moment at IBM Watson Health where a solution that worked perfectly in controlled conditions failed in patients’ homes, forcing us to rethink the actual customer we were serving. That kind of learning is where real innovation happens. The response to the episode has been strong, over 2,000 downloads in the first 3 days, which tells me this conversation is resonating with leaders navigating the same challenges. If you’re working to bring innovation into an organization that’s optimized for execution, you may find it useful. 🎧 Listen here: https://lnkd.in/g6NBvZff Vasco also wrote an incredible, thorough blog about our episode that you can read here: https://lnkd.in/gR8K8B4y

If you don’t have a product innovation process, you aren’t really innovating. You’re just gambling. The question you should be asking … Are you innovating proactively or reactively? Because without a clear, repeatable innovation process, what should be prioritized and scaled never actually is. Instead, product leaders end up … • Designing user journeys on the fly • Cramming in features to please everyone • Acting like air traffic control between customers, engineers, and execs • Constantly putting out fires instead of preventing them It feels busy. It feels essential. But it’s not strategic. And it’s not sustainable. The cost? • Poor product outcomes • Delayed launch, releases or delivery • Team frustration • Burnout • Burning cash and expensive resources • Customer churn • And worst of all … a fuzzy path to Product-Market Fit (PMF) If any of this sounds familiar… • Sprint planning feels chaotic • Your product team’s unclear on where the product is headed • Engineers are stuck rebuilding what wasn’t thought through the first time It might not be a dev team problem. It might be a product innovation gap. Without a structure to validate, prioritize, and prototype before building, you’re flying blind. Innovation isn’t magic. It’s a process, that fuels confidence, aligns your team, and increases your odds of building something that actually matters. If you don’t fill this gap intentionally, it will fill itself with chaos. And that chaos? It will quietly drain your time, budget, and morale, until there’s nothing left to scale.

A lot of organisations create chaos and call it innovation but miss one essential point ... - They run hackathons without strategy. - They start with ideas before understanding problems. - They proudly generate hundreds of sticky notes - They call it agile, but it’s aimless. - They say “fail fast,” but never learn. - They launch “labs” with no link to core business. They have no clear process, no guidance, and no overview—just a lot of enthusiastic noise. And when leadership asks, “What’s the ROI?”: silence. Innovation isn’t about looking busy or creative. It’s about making real, valuable change—on purpose. Innovation is all about creating strategic impact. For that you need: STRUCTURE! Structure to transfor new technologies and ideas into reality. Without structure you’re not innovating. You’re just making innovation theatre. Ps. When you want to structure the fuzzy front end of innovation, you might check out the proven FORTH Innovation Method. Link and map in the comments. #Innovation #CorporateInnovation #Strategy #DesignThinking #InnovationTheater #BusinessTransformation #InnovationFails #InnovationLeadership #InnovationStrategy #InnovationManagement #BreakingInnovationBarriers

When people talk about innovation, they usually mention creativity. But what they miss: Is the discipline behind it. James Dyson built 5,127 prototypes before his vacuum finally worked. Not because he was obsessed. Because he had a method. A structured way to test, learn, and improve. That’s what most people overlook. They treat problem-solving as intuition or improvisation. Something fueled by genius or luck. But the truth? Breakthroughs happen when you follow a process: Even when you’re tired. Even when it’s not working. Even when no one’s watching. That’s what I’ve seen in classrooms, boardrooms, and across the pages of Cracked It!. And it’s exactly what I teach: A 4-step method used by the best strategy consultants in the world: State the problem. Structure it. Solve it. Sell it. The best problem-solvers aren’t the smartest. They’re the most disciplined. Because structure beats guesswork, every time.

𝗥𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗵𝗮𝘀 𝗮𝗻 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻. It forces you to accept that real value doesn’t come from adding new capabilities, but from changing how the business actually works. That’s why so many AI initiatives stall. Not because the technology doesn’t work. But because the business system around it stays the same. 𝐀𝐈 𝐨𝐧𝐥𝐲 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐦𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐦𝐩𝐚𝐜𝐭 𝐰𝐡𝐞𝐧 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐫𝐞𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐚𝐭 𝐬𝐲𝐬𝐭𝐞𝐦: ✔️ how decisions are made and at what speed ✔️ which process steps disappear instead of being automated ✔️ how human judgment and machine intelligence work together ✔️ how trust, risk, and governance are embedded by design ✔️ and where accountability truly sits in an AI-driven operating model That’s not (just) optimisation. That’s reimagination. 𝐎𝐯𝐞𝐫 𝐭𝐡𝐞 𝐩𝐚𝐬𝐭 𝐦𝐨𝐧𝐭𝐡𝐬, 𝐨𝐮𝐫 𝐒𝐰𝐢𝐬𝐬 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐭𝐞𝐚𝐦 𝐡𝐚𝐬 𝐫𝐮𝐧 𝐚 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐫𝐞𝐢𝐦𝐚𝐠𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐬𝐡𝐨𝐩 𝐟𝐨𝐫𝐦𝐚𝐭 𝐰𝐢𝐭𝐡 𝐬𝐞𝐯𝐞𝐫𝐚𝐥 𝐜𝐥𝐢𝐞𝐧𝐭𝐬. What stood out every single time was not the number of ideas – but that the sessions actually converged to outcomes. Because reimagination only works when it follows a disciplined, step-by-step process: 1️⃣ clarity on what success really means 2️⃣ a shared target state 3️⃣ explicit trade-offs 4️⃣ coherent business responses and 5️⃣ commitment to prioritisation, ownership, and next moves 𝐓𝐡𝐚𝐭 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐭𝐮𝐫𝐧𝐬 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐧 𝐢𝐧𝐭𝐨 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧. At this point, some people ask whether this is too structured for creativity. In our experience, it’s the opposite. Structure doesn’t limit creativity - it creates a safe space for bold thinking, and then forces it to become real. 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐲 𝐭𝐡𝐞𝐬𝐞 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐟𝐞𝐞𝐥 𝐥𝐢𝐠𝐡𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐦𝐨𝐦𝐞𝐧𝐭, 𝐛𝐮𝐭 𝐥𝐞𝐚𝐝 𝐭𝐨 𝐯𝐞𝐫𝐲 𝐬𝐞𝐫𝐢𝐨𝐮𝐬 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬. Now, if we tell clients they need to reimagine, we should be willing to do the same ourselves. That’s why this week I brought parts of my own leadership team together and applied the same discipline internally – deliberately, and without shortcuts. 𝐓𝐡𝐢𝐬 𝐲𝐞𝐚𝐫, 𝐰𝐞’𝐫𝐞 𝐠𝐨𝐢𝐧𝐠 𝐚𝐥𝐥-𝐢𝐧 𝐨𝐧 𝐀𝐈. Not as a collection of isolated use cases, but as a way to rethink decisions, processes, and value creation end-to-end. This is exactly what we help clients with: AI & Data strategy, data modernisation, AI Factory at scale, and increasingly, reimagining entire value chains so AI delivers sustainable business outcomes. I’ll share some of the transformation learnings over the coming months. And if you’re looking for a way to help your leadership team reimagine the future of your business – with structure, discipline, and real outcomes – feel free to reach out. 📽️ 𝑉𝑖𝑑𝑒𝑜 𝑐𝑟𝑒𝑑𝑖𝑡𝑠 𝑡𝑜 𝑐𝑎𝑟𝑝𝑒𝑛𝑎𝑎𝑟𝑡𝑝𝑎𝑔𝑒

What Bell Labs Can Still Teach Us About Innovation This week marks the 100th anniversary of Bell Labs. How did one organization produce the transistor, the solar cell, the laser, cellular networks, UNIX, information theory, and eleven Nobel Prizes? Luck alone can't explain it. The answer lies in three structural conditions most organizations today are unwilling or unable to replicate. Situational advantages: start with a problem-rich environment Nobel laureate Arno Penzias once said of Bell Labs: "You have to understand, this was a problem-rich environment." Bell Labs wasn't doing blue-sky speculation. It was responsible for designing and improving an entirely novel communications network. How do you get a phone call from New York to London? How do you scale to a million calls a day? Every answer generated new questions. The engineers weren't searching for problems — the problems found them. Budget predictability: the freedom to think in decades Being part of AT&T gave Bell Labs something rarer than money alone: predictable money. By the early 1980s, its annual budget was roughly $2 billion (about $7 billion today). The cellular phone concept was first sketched in the late 1940s and not field-tested until the late 1970s — a thirty-year arc. No quarterly earnings call could have sustained it. Budget continuity wasn't just an input; it was a precondition. The monopoly paradox: no competition, more innovation We assume competition drives innovation and monopolies suppress it. Bell Labs is perhaps the most powerful counterexample in tech history. Without competitive pressure, engineers had the luxury of working through hard problems over long timeframes. And the federal arrangement was clever: AT&T kept its monopoly in exchange for licensing Bell Labs patents at modest fees, so innovations flowed outward even as the Labs kept generating new ones. The lesson endures: sustained innovation requires structural conditions — not just talented people. It requires investing in the unknown, on long timescales, without demanding immediate returns. A century later, that remains the hardest thing to build. See Jon Gertner's excellent piece in the WSJ (link in comments) marking Bell Labs' centennial — and his book The Idea Factory, essential reading on this subject.