Innovation Traps in Large-Scale Projects

Your organization says it wants innovation. Your processes suggest otherwise. I watched a brilliant executive spend 6 weeks getting approval to test a $50/month software tool. By the time she got the green light, two competitors had already deployed similar solutions and moved ahead. This isn’t stupidity. It’s institutional logic taken to its absurd conclusion. Here’s what’s really happening: Every approval layer was added for good reasons. Every committee was formed to prevent real disasters. Every process was implemented to solve actual problems. But collectively, they’ve created something no one intended: organizations so protected from making bad decisions that they can’t make ANY decisions. The “We Already Have a Process” Problem Walk into any corporate meeting about innovation and listen to the language: • “How does this align with our existing governance framework?” • “What’s the ROI justification for deviating from proven methodologies?” • “We need to ensure this integrates with our current compliance requirements.” These aren’t questions. They’re defensive mantras. The underlying message is clear: If your innovation doesn’t fit our existing framework, the problem isn’t with the framework—it’s with your innovation. The Expert Authority Trap The CIO who built their career preventing security breaches. The CFO who optimized cost structures. The Legal counsel who knows every compliance pitfall. These aren’t obstructionist bureaucrats. They’re experts whose professional identity depends on understanding why things might go wrong. But expertise optimized for preventing known problems becomes a barrier to discovering unknown opportunities. What Actually Works The organizations winning this game don’t eliminate their governance systems—they create parallel tracks. → High-risk decisions get rigorous 6-week evaluations → Low-risk experiments get 6-day pilot approvals → Different types of innovation get different types of oversight The Real Challenge This isn’t about process—it’s about identity. When systems change, people must grapple with fundamental questions: What’s my role? What’s my value? Who am I in this new world? The IT professional trained to prevent problems must learn to enable possibilities. The finance analyst who eliminated costs must develop intuition about when spending money to learn is the most economical choice. The Bottom Line Organizations that thrive in the next decade won’t choose between innovation and control. They’ll master both simultaneously. They’ll develop what I call “institutional ambidexterity”—the ability to be stable AND adaptive, careful AND experimental, systematic AND creative. The question isn’t whether your organization can change. It’s whether your organization can learn to change intelligently. What’s the biggest innovation killer in your organization? Share your story in the comments—I read every one.

Inside the project graveyard (a.k.a. where momentum goes to die) Let’s be honest most transformation roadmaps lie. They show clear milestones, tidy swim lanes, and clean Gantt charts. What they don’t show? → The initiatives that got quietly killed → The pilots that never scaled → The money burned without measurable value I’ve led large-scale programs and advised executives on ones headed for disaster.  So here’s what actually sends projects to an early grave: 1. “𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭” 𝐭𝐡𝐚𝐭 𝐦𝐞𝐚𝐧𝐬 𝐚 𝐤𝐢𝐜𝐤𝐨𝐟𝐟 𝐯𝐢𝐝𝐞𝐨 𝐚𝐧𝐝 𝐫𝐚𝐝𝐢𝐨 𝐬𝐢𝐥𝐞𝐧𝐜𝐞 ↳ If you’re not showing up in the room, you’re not leading. Modernization needs air cover, not cheerleading. 2. 𝐌𝐢𝐝-𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐩𝐢𝐯𝐨𝐭𝐬 𝐟𝐫𝐨𝐦 “𝐦𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐞 𝐭𝐡𝐞 𝐜𝐨𝐫𝐞” 𝐭𝐨 “𝐥𝐚𝐮𝐧𝐜𝐡 𝐀𝐈 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰” ↳ You can’t bolt innovation onto a broken foundation. And you definitely can’t do both at once with the same team and budget.  3. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐭𝐞𝐚𝐦𝐬 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐟𝐨𝐫 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠, 𝐧𝐨𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲 ↳ 10 slide decks. 0 decisions. If your governance model can’t say no fast, your program is already on fire. 4. 𝐕𝐚𝐠𝐮𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐝𝐢𝐬𝐠𝐮𝐢𝐬𝐞𝐝 𝐚𝐬 “𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲” ↳ “We’ll build capabilities we can use across the enterprise.”  Translation: We don’t actually know what success looks like. 5. 𝐏𝐨𝐥𝐢𝐭𝐢𝐜𝐬 𝐨𝐯𝐞𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭   ↳ When leaders protect turf instead of solving problems, your roadmap becomes a grave plot. If you’re not learning from the graveyard, you’re repeating it. #modernization #insurtech #insurance

Running an innovation program for a large company is a treacherous position to be put in. Several years ago, a business partner and I were building an offering to large companies that would help them run their innovation programs. We never were able to get it off the ground, but it gave me the opportunity to conduct customer development interviews with about 50 heads of innovation at large companies. In addition, I had been personally involved with innovation programs at several large companies before then. Here is what I've learned: ▶ Most innovation leaders "have greatness thrust upon them," as the saying goes. Which is to say, they kind of just get assigned to "just go figure it out." Few, if any, have real startup experience, to say nothing of experience running what is essentially an entire VC portfolio. ▶ Most senior executive teams who sponsor the creation of an innovation program do not really know what that means. They have achieved their positions of power by operating very large scaled organizations, not by being creative and taking risks. They don't necessarily think like innovators. ▶ One of the most important aspects of success in an innovation program is finding and recruiting the right people. They can sourced internally or externally, but they have to be smart, scrappy, entrepreneurial types. The irony is that people like that don't want to work at your large company, even the innovation lab. To be successful, if you are finding yourself in this position, or (bless you) you are one of the executives who is sponsoring it, you must start with a heavy dose of managing up to the CEO and the board, setting appropriate expectations. Those expectations are that it will be years before anything with measurable ROI comes from this effort. You will try many things and they will fail. That is OK as long as you learn why along the way. The people who work in the innovation program must be protected at all times from the daily operational work of the organization. You cannot steal talent for special projects etc. You must have a very structured system in place for sourcing, vetting, validating, and quickly killing a very large number of ideas. There is no one great idea that is going to make the whole thing work. There is a huge mindset shift required in the larger organization in order to make it safe to run an innovation program. The CEO, leadership team, and the board need to make this a priority, not a side project. If you want to talk more about your situation in your innovation lab, give me a ping. I would be happy to tell you many stories, both success and failure, that might help you avoid some of the biggest landmines. Godspeed!

𝗣𝗮𝘁𝗵 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆, 𝗶𝘀 𝗶𝘁 𝗮 𝗧𝗿𝗮𝗽 𝗼𝗿 𝗧𝗼𝗼𝗹? It's not just legacy systems that hold companies back. It's poor architecture choices, resistance to change and the failure to challenge past assumptions. Path dependency, where past decisions shape future choices isn't inherently bad. It can provide guard-rails for standardisation and establish a solid foundation for incremental innovation. When left unquestioned, it limits innovation and discourages the evaluation of better processes, tools and methods. And it's not just about "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮𝗹𝘄𝗮𝘆𝘀 𝗱𝗼𝗻𝗲 𝗶𝘁 𝘁𝗵𝗶𝘀 𝘄𝗮𝘆" or "𝘄𝗲 𝗰𝗮𝗻𝗻𝗼𝘁 𝗱𝗲𝘃𝗶𝗮𝘁𝗲 𝗳𝗿𝗼𝗺 𝗮 𝘀𝗲𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆". Even logical decisions derived from past choices can create constraints. 𝗖𝗵𝗮𝗻𝗴𝗲 𝗰𝗮𝗻 𝗯𝗲 𝗿𝗲𝗮𝗹𝗹𝘆 𝗵𝗮𝗿𝗱, 𝗯𝘂𝘁 𝘀𝘁𝗮𝘆𝗶𝗻𝗴 𝘀𝘁𝘂𝗰𝗸 𝗰𝗮𝗻 𝗯𝗲 𝗲𝘃𝗲𝗻 𝗵𝗮𝗿𝗱𝗲𝗿. A common example in Procurement Technology stacks and architecture is assuming that ERP brands dictate Procurement solutions. While integration benefits exist, this shouldn't be leading to an automatic dependency if better fitting solutions are available. Several factors can turn path dependency into 𝗮 𝗧𝗿𝗮𝗽: ◾️𝗦𝘂𝗻𝗸 𝗰𝗼𝘀𝘁 𝗳𝗮𝗹𝗹𝗮𝗰𝘆 by holding onto outdated systems or architecture decisions because “𝘄𝗲’𝘃𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗶𝗻𝘃𝗲𝘀𝘁𝗲𝗱 𝘁𝗼𝗼 𝗺𝘂𝗰𝗵.” ◾️𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗱𝘂𝗲 𝘁𝗼 𝗼𝘃𝗲𝗿-𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 such as S2P suites or ERP setups that make every new integration a nightmare, adding technical debt. ◾️𝗙𝗮𝗺𝗶𝗹𝗶𝗮𝗿𝗶𝘁𝘆 𝘁𝗿𝗮𝗽 resisting new processes instead of embracing smarter, more efficient alternatives because it's hard to change people. ◾️𝗕𝗶𝗮𝘀𝗲𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 by using historical projects, past failures or data to guide new decisions but never questioning if it’s still relevant. When approached strategically, path dependency can be an advantage and a real benefit. For instance, instead of locking into rigid systems, a modular, composable architecture can turn into a great asset & 𝗧𝗼𝗼𝗹 supporting: ◾️𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 of an architecture through modular, API-based SaaS solutions while keeping the ERP as a backbone. ◾️𝗙𝗼𝗿𝘄𝗮𝗿𝗱-𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 to refine spend and supplier analytics rather than just solely relying on historical spend patterns. ◾️𝗦𝗺𝗮𝗿𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 by enhancing process efficiency gradually in existing solutions rather than discarding everything at once. ◾️𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 through a composable architecture, allowing to complement what you have with modern features - like my friends at Olympe.io offer. In summary, whether path dependency becomes a trap or a tool depends on how we handle it. The key? Recognising when past choices are guiding you forward and when they’re holding you back. So, is your Procurement Technology architecture creating a path dependency as an asset or are you locked-into a trap?

I’ve seen incredible AIoT visions crash and burn, not because the tech wasn’t capable, but because crucial details were overlooked. It’s frustrating to watch promising projects silently unravel. At Whitespectre, navigating complex deployments has taught us that these aren't just technical glitches; they're often fundamental blind spots. These 10 hidden traps silently kill AIoT projects long before launch. Here’s how to avoid them: ➞ 1. Starting with AI, not the problem Too many teams build models without knowing the real need. AI should solve outcomes, not chase metrics. ➞ 2. Bad data at the edge Edge data is often noisy, missing, or raw. If you don’t calibrate or structure it, nothing downstream works. ➞ 3. Prototypes that ignore real-world constraints The lab isn’t the field. Bandwidth, heat, casing - if you don’t test for it, you’ll fail at deployment. ➞ 4. Cloud-only thinking in edge scenarios Latency kills decisions. If it must act in real time, push inference to the edge where speed and privacy live. ➞ 5. No feedback loops AI needs to learn after deployment. If you don’t capture errors and overrides, your model will degrade fast. ➞ 6. Overcomplicated stacks Bloated layers = fragility. Simplify your stack to boost reliability and observability. ➞ 7. Limited context engineering AI agents can’t reason if context is missing. Metadata like roles, time, and device status must be engineered in. ➞ 8. Ignoring security Plaintext passwords and open ports? That’s not just sloppy - it’s dangerous. Secure from the ground up. ➞ 9. Models don’t match hardware Running heavy models on low-power MCUs leads to crashes. Always test latency, memory, and compatibility early. ➞ 10. No real-world validation What works in perfect conditions will break in the wild. Simulate noise, damage, collisions - then validate again. The most successful AIoT systems don’t start with models. They start with design for reality. ♻️ Repost if you’re tired of lab-perfect AI demos that die in production ➕ Follow me, Nick Tudor, for grounded insights in AI + IoT + edge deployment

HOW TO CREATE SAFE SPACES FOR UNSAFE IDEAS You hire brilliant people and tell them to innovate. Then you make it impossible for them to do so. Most companies develop an immune system that rejects new ideas like they're some kind of virus. Here are the five innovation killers you need to spot and eliminate: KILLER #1: DEMANDING CRYSTAL BALL ACCURACY You want detailed business cases for projects that are inherently uncertain. The fix: Create different approval processes for exploration vs. execution. Exploration projects get smaller budgets and you measure success by what you learn, not what you earn. KILLER #2: BEING SCARED OF EVERYTHING Your processes are designed to avoid any downside risk, which also kills any upside potential. The fix: Separate "experiments you can't afford to mess up" from "experiments you can't afford not to try." Different projects, different comfort levels with risk. KILLER #3: MAKING INNOVATION FIGHT FOR SCRAPS Innovation projects have to compete with your proven money-makers for resources. The fix: Set aside dedicated innovation resources. 10% of engineering time, 5% of budget, just for projects where you don't know what'll happen. KILLER #4: JUDGING EVERYTHING ON QUARTERLY RESULTS You evaluate innovation projects on the same timelines as your day-to-day operations. The fix: Innovation gets measured by learning cycles, not calendar quarters. Success is about insights you gain, not deadlines you hit. KILLER #5: THINKING FAILURE MEANS SOMEONE SCREWED UP You define success as "execute the original plan perfectly." The fix: Success becomes "figure out what works as fast as possible." Changing direction gets celebrated, not punished. The framework that can transform your innovation culture: EXPLORE → EXPERIMENT → EXECUTE EXPLORE PHASE: Small budget, big questions. Win = quality insights. EXPERIMENT PHASE: Medium budget, specific hunches. Win = fast validation (or fast failure). EXECUTE PHASE: Full budget, proven concept. Win = flawless delivery. Different phases, different rules, different ways to win. Companies don't lack innovative ideas. They lack innovative environments. QUESTIONS TO DIAGNOSE YOUR INNOVATION IMMUNE SYSTEM: ❓How many good ideas die in approval meetings instead of real-world tests? ❓What percentage of your "failed" projects actually teach you something valuable? ❓How long does it take to get approval for a $10K experiment vs. a $10K efficiency upgrade? ❓Do your best people feel comfortable pitching risky ideas? If your best employee came to you tomorrow with a risky but potentially game-changing idea, would they feel safe pitching it? *** I’m Jennifer Kamara, founder of Kamara Life Design. Enjoy this? Repost to share with your network, and follow me for actionable strategies to design businesses and lives with meaning. Want to go from good to world-class? Join our community of subscribers today: https://lnkd.in/d6TT6fX5 

There are many traps we can fall into when it comes to innovation. Being aware of them is critical to avoiding them. We have helped customers literally create billion dollar verticals in just a few years - but a key factor in their success was avoiding a number of common traps. Below are some of the most common traps we have seen organizational leaders (or committees) fall into while evaluating early-stage ideas and projects. *** Putting a strong project on delay. “Great project, but not the right time. We will kickstart this in a few months when XYZ has happened.” BUT: The project will lose its momentum and likely its champions. *** Folding it under, or combining it with, an ongoing project. “This fits really well with ongoing project X, so let's make it part of that.” BUT: You will lose the speed, uniqueness and autonomy of the team and their idea. *** Handing it over to another team, with no representation from the original team. “We have the perfect team with the right competence, they are ideal for this.” BUT: The new team is not invested and has no context of the decisions made. The project will fade into obscurity. *** Giving the project a big budget, too soon, in order to execute it quickly. “LOVE this idea. Let’s just clear a ton of money for this team right away.” BUT: The project will take off in a direction that seems reasonable at the time but is likely still wrong. *** Someone - to whom the idea does not make sense - takes ownership (and chokes it). “Honestly, we do not have a right to play in this space. It’s a waste of time. Let’s slo-mo kill this one.” BUT: Catastrophic for innovation culture. *** Squeeze the new idea into an existing business or operating model. “We know exactly how to do this - we have the templates, the channels, the processes - it's GO time.” BUT: The product will lose its uniqueness and what made it innovative in the first place. There are many others, of course, any that come to mind for you? #innovation, #execution, #medicieffect Ivan Tornos, JehanZeb Noor, Orsa Britton, Joe White, Chris Yeh, Minerva Tantoco, Anders Gustafsson, Regina Curry, Bruce Stephenson, Rita McGrath, Scott D. Anthony, Hondo Geurts, Dimitris Bountolos, Marie-Claire Barker, Marc Allen, Jonathan Beane, Zhen Su, MD MBA, Paulash Mohsen,

Winning the wrong game is still losing. Many corporates are trapped in the “Success to the Successful” loop: doubling down on what works, starving what’s new, and calling it strategy. But optimizing the core while ignoring the edge isn’t strategy. It’s slow-motion self-sabotage. The next S-curve doesn’t wait for your budget cycle. By the time you decide to act, the market has moved on, and you’re left overpaying for what you could have built. At MING Labs, we see this pattern too often: innovation teams pitching endlessly for scraps, early-stage ideas filtered through today’s P&L, and bold visions undermined by legacy metrics. Escaping the success trap requires structural change: - Allocating budgets for exploration before the business case. - Designing ventures to operate independently from the core. - Protecting innovation from the gravitational pull of the existing business. It’s not about adding innovation theater. It’s about rewiring the system to allow for true exploration. Because in a world of constant change, clinging to past success is the riskiest move of all. https://lnkd.in/eVc_zv7F #business #strategy #innovation #venture

Big ideas fail when you start too big. I’ve seen it too many times. Companies think BIG, but instead of starting small and learning fast, they dive headfirst into massive projects. The result? ❗️ Projects too big to fail and too complex to manage. ❗️ Siloed teams focused on local priorities, not shared outcomes. ❗️ Metrics that track outputs instead of driving real business results. Innovation doesn’t stall because of bad ideas. It stalls because of how teams approach scaling them. 🔹 DESCALING is the answer. It’s about breaking big ideas into small, manageable bets—where teams can learn, iterate, and align on outcomes that matter. I break down 7 common mistakes that derail big projects and share how to fix them in my article. Curious to see where your teams might be going wrong? Read the full article here: https://lnkd.in/gCQDPqy #descaling #innovation #leadership #productdevelopment #scalingsuccess