Innovation Management in Engineering

Your engineers are brilliant. That's why they keep solving the same problem at different facilities. Over and over. Without knowing someone already figured it out. This isn’t an intelligence problem. It’s an infrastructure problem. Plant A has brilliant engineers.  They found a quality issue costing $8K a month.  Spent three weeks finding the root cause.  Built a smart solution.  Saved $100K a year.  Documented everything.  Problem solved. Six months later, Plant B found the same issue.  Did the same analysis.  Built the same solution.  Saved the same $100K.  Documented it separately.  Problem solved again. Plant C? Also brilliant.  They’re discovering the same issue right now.  Starting the same process.  They’ll solve it soon, for the third time. Same company.  Same brilliance.  Zero knowledge sharing. Each plant keeps its own notes.  No central system.  No easy search like “Has anyone solved this before?”  No alerts when similar problems show up.  No way to turn local wins into company standards. So every plant starts from scratch.  And your best practices stay trapped. Spreadsheets on local drives.  Old email threads.  PowerPoints buried in folders.  Knowledge stuck in people’s heads. Hundreds of great ideas are locked away.  While others waste time reinventing them. That’s lost time, lost money, and lost progress. The best manufacturers treat knowledge like inventory.  You wouldn’t let one plant hoard materials while another runs short.  So why let one plant hoard solutions? When Plant A solves something, it should go into a shared system.  Tagged by equipment, process, and problem.  Searchable for everyone.  Alerting others when similar issues appear.  Scalable across all plants. That’s how local wins become company standards. Plant A’s $100K idea  becomes $300K when shared with B and C.  Same effort.  Triple the impact. In three weeks, all plants could be aligned, instead of six months of duplicate work. Your engineers stop reinventing and start innovating.  New engineers learn faster.  The whole company gets smarter. You already have brilliant engineers.  You already have brilliant solutions.  Now it’s time to multiply that brilliance, not trap it. Because every month knowledge stays isolated,  your competitors move ahead.  They’re solving once and scaling everywhere. Your engineers are brilliant.  Your solutions are excellent.  Your knowledge sharing is broken. Fix the infrastructure,  and brilliance multiplies. P.S. If your best practices are trapped on islands,  let’s talk about building the system that sets them free.  DM me “KNOWLEDGE.”

𝗠𝗼𝘃𝗶𝗻𝗴 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿𝘄𝗮𝗿𝗱: 𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗔𝗜 We say AI is transforming everything but ask your engineering team, and you’ll hear a different story. The promise is huge. But the reality? Still clunky, siloed, and hard to scale. 🔍 So where’s the disconnect? Its how we’re trying to fit tech into complex, human workflows. Arthur D. Little’s report pulls back the curtain on why AI adoption in R&D and engineering still lags behind and what it’ll take to turn potential into performance. 👇 If you work in innovation, product, or tech strategy, this one’s worth your time. 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 & 𝗦𝗶𝗴𝗻𝗮𝗹𝘀: 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲, 𝗻𝗼𝘁 𝗔𝗜. Fewer than 15% of firms report maturity in AI for engineering, despite 900+ proven use cases. Why? Lack of skills, scattered data, and trust issues are the true bottlenecks. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗶𝘀𝗻’𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁-𝗳𝗶𝗿𝘀𝘁 𝗮𝗻𝘆𝗺𝗼𝗿𝗲, 𝗶𝘁'𝘀 𝘀𝘆𝘀𝘁𝗲𝗺-𝗳𝗶𝗿𝘀𝘁. Smart, functionalized solutions require complex integration not just deeper expertise. From digital twins to modular ecosystems, system thinking is the new core skill. 𝗟𝗲𝗴𝗮𝗰𝘆 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗼𝗻’𝘁 𝘀𝗰𝗮𝗹𝗲. Old ways of working can't handle today’s rising complexity, regulation, and user expectations. AI adoption without cultural redesign is just automation, not transformation. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀. Engineers don’t reject AI, they reject tools that don’t fit real workflows. Success comes from user-centered design, not tech push. Motivation, ease, and relevance matter. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 + 𝗨𝘀𝗲𝗿 𝗟𝗮𝘆𝗲𝗿𝘀 = 𝗥𝗲𝗮𝗹 𝗜𝗺𝗽𝗮𝗰𝘁. The winning orgs build a dual-layer AI portfolio: • Strategic: Must-win battles aligned with innovation goals • User: Everyday tools mapped to real pain/gain points 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁? 𝗧𝗵𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗲𝗱 𝗟𝗮𝗯 𝗼𝗳 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲. Democratized AI tools Agile, ambidextrous teams Clean, connected data Scalable, interoperable tech stacks 📌 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲 AI won’t replace engineers. But engineers who use AI wisely will outpace those who don’t. ❓Is your AI strategy empowering your engineers or overwhelming them? Oriol Caudevilla | Prof. Dr. Ingrid Vasiliu-Feltes | Vikram Pandya | Francesco Burelli | Arjun Vir Singh | Jaspreet Bindra | Chintan Oza | Dr. Satyam Priyadarshy | Dr. Utpal Chakraborty(PhD)) | Dr Ritesh Jain | Dr. Ram Kumar| Prasanna Lohar | Dr. Takahisa Karita | Dan Lohrmann | Syed Musheer Ahmed | Sam Boboev | Victor Yaromin | Saleh ALhammad | Dr. Paritosh Basu | Ian Gauci | Dr. Sudin Baraokar | ChandraKumar R Pillai | Dr Mukund R. | Dr. Tinoo Nandkishore Ubale, #Innovation #AI #AIinEngineering #InnovationStrategy #R&D #FutureOfWork #Engineering #DigitalTransformation

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 

You're going to waste 75% of your innovation budget. Not because your team isn't talented. Not because customers are unpredictable. Not because the market is too competitive. Because you're spending time and money developing the wrong products when you should be spending time upstream on understanding what are the right products to build. Here's the math: Average company: → $10M innovation budget → 10 products in development → 8 will fail (80% failure rate) → Wasted: $8M on products that don't work → Cost to fix failed products post-launch: 100X more than getting it right initially Alternative approach: → Reallocate $2M upstream to opportunity identification → Discover all unmet customer outcomes before ideation → Develop only products addressing validated opportunities → Success rate: 86% instead of 17% → Saved: $6M in avoided failures → ROI on upstream investment: 300% W. Edwards Deming proved this in manufacturing: "You cannot inspect quality into a product. It must be built in from the design." Same principle for innovation. You cannot iterate your way to product-market fit. You must design for it from the beginning. The difference between innovation as art and innovation as science: Art approach: → Generate ideas → Build prototypes → Test with customers → Iterate based on feedback → Repeat until something works (if ever) → 17% success rate Science approach: → Map customer's job-to-be-done → Identify 50-150 desired outcomes → Quantify which outcomes are underserved → Design solutions addressing multiple unmet needs → Launch knowing it will work → 86% success rate The companies pulling ahead aren't more creative. They're more systematic. They've stopped treating innovation like creative expression and started treating it like process engineering. If three-quarters of your product development efforts will fail, you have plenty of budget that can be reallocated to preventing those failures. Are you inspecting innovation into products through trial and error—or designing it in through systematic process?

85% of manufacturing leaders want more innovation. Only 6% are satisfied with their results. This massive gap isn't about capability. It's about fear. A VP of Engineering recently confessed to me: "My team has brilliant ideas in private conversations. Those same ideas vanish completely in our formal innovation meetings." Sound familiar? The disconnect isn't about talent or technology. It's about psychological safety. When failure means blame, innovation dies silently. Here's what I discovered early in my career working with high-tech manufacturers: structured experimentation doesn't just drive technical outcomes. structured experimentation fundamentally transforms team psychology. Let me share one innovation secret: EXPERIMENTS CAN'T FAIL The smartest teams rigorously separate experiments from implementations. ➡️ "An experiment that disproves our hypothesis succeeded at its job." I often tell engineers " A null result is still a result." ➡️ They celebrate insights gained, not just ideas validated. ➡️ They ask "What did we learn?" not "Did it work?" One equipment manufacturer that I worked with made this shift explicit. In their innovation meetings, they stopped asking teams to "pitch solutions." Instead, they asked: "What experiment could answer our biggest unknown?" The result? Soon everyone was participating. This simple shift eliminates the fear of suggesting ideas that might not work. It transforms the question from "What if I'm wrong?" to "What will we discover?" The psychology is everything. When people know their ideas won't be judged as right or wrong only as experiments that generate data they feel safe to contribute more boldly. What's really holding back innovation in your organization: lack of ideas or lack of safety to try them? Tomorrow I'll share how the smartest teams compress learning cycles from weeks to days. It's not what most leaders expect. Follow me so you don't miss part 2 tomorrow. #PersonalGrowth #PsychologicalSafety #Experiment

As engineering orgs grow, creativity often dies. More users = more complexity. More complexity = more processes. Before you know it, the innovation that built your product is buried under red tape. At GitHub, we’ve experienced this firsthand. Serving 100+ million developers means constantly balancing stability vs. innovation. So how do you protect creative space while managing complexity? Matt Nigh and I have a new article out on GitHub’s Executive Insights blog—on how to protect innovation as your engineering org scales. Here’s what works: ✅ Standardize to accelerate, not restrict: Set clear baselines so teams can move fast without reinventing the wheel. ✅ Protect flow time: Developers do their best work uninterrupted. Cut the unnecessary syncs. ✅ Automate the grind: Eliminate toil. Free up mental space for innovation. ✅ Invest in safe experimentation: Feature flags & smart risk-taking enable innovation without breaking production. ✅ Use AI as a creativity multiplier: Copilot keeps devs in flow, reduces context switching, and unlocks new ideas. Read the full breakdown via the link: https://lnkd.in/gAWBJUin

Innovation is the output of repeatable behaviors, reinforced every day. When those behaviors are designed intentionally, innovation becomes predictable. Here are five that matter. 1. Make curiosity non-negotiable. Curiosity is a leadership trait, and in innovative organizations, questions are valued as much as answers. People are encouraged to notice misalignment, challenge assumptions, and ask why things exist the way they do. When curiosity is rewarded, stagnation has nowhere to hide. 2. Normalize experimentation at a small scale. Breakthroughs come from disciplined tests: clear hypotheses, limited scope, fast feedback. When experimentation becomes routine, teams stop protecting ideas and start validating them. Risk goes down because learning speeds up. 3. Treat past innovation as data. Most companies either celebrate the wins or bury the losses. Innovative ones do neither. ↳ They look closely at what worked. ↳ They’re honest about what didn’t. ↳ They pull patterns from both, without rewriting the story. That’s how judgement gets built. And over time, judgement grows faster than creativity ever will. 4. Tighten feedback loops relentlessly. Innovation dies when teams operate in isolation. Strong leaders keep ideas close to reality. Fast feedback prevents wasted effort and forces ideas to evolve or be abandoned early. Learning velocity matters more than idea originality. 5. Engineer diversity of thought. Homogeneous teams refine what already exists, and diverse teams rethink the problem itself. When ideas are challenged from different angles, unseen risks show up sooner, and opportunities surface earlier. This is an operational advantage. Innovation becomes predictable when leaders stop waiting for inspiration and start building systems that generate insight. Innovation requires discipline. And the organizations that understand this build environments where breakthroughs are inevitable.

Many organizations approach innovation the way they approach budgeting or operations. They create roadmaps, timelines, and committees designed to produce breakthroughs on schedule. But the history of technology suggests something different. Most meaningful innovations do not arrive neatly on a calendar. They appear unexpectedly. A new idea. A technical breakthrough. A surprising connection between two things that previously seemed unrelated. The real challenge is making sure your organization is ready when those moments appear. The companies and institutions that consistently innovate tend to invest early in talent and technical capability. They build cultures where experimentation is encouraged and where people are willing to test new ideas. They maintain the flexibility to pursue unexpected opportunities and move quickly when promising ideas appear. Innovation rarely begins as a fully formed plan. More often it begins as a possibility that only a few people recognize at first. The advantage goes to the organizations that have prepared themselves to recognize that moment and act on it. You may not be able to schedule inspiration. But you can build teams, systems, and cultures that are ready when it shows up. #SchmidtSights

MIT published a study showing 95% of generative AI pilots in enterprises fail. Large organizations push new technology into old processes and conservative cultures. That approach kills projects before they have a chance. Real outcomes come when innovation gets managed as a discipline, not as a side effort. Innovation requires structure, a portfolio mindset, and the same discipline that VCs use—make bets, track progress, scale only what works. This is the foundation we are building at Innovera.ai. A system to help companies run innovation the right way, so technology actually delivers impact.

The AI gap in engineering isn't just about tools – it's about mindset. I've been talking with VPs of Engineering who tell me the same story: their best people are drowning in documentation while competitors ship faster with smaller teams. Here's what I'm seeing: A mechanical engineer at a Fortune 500 company spent three days tracking down design rationale from a project completed 18 months ago. The knowledge existed, but it was scattered across emails, CAD comments, and someone's personal notes. Meanwhile, a startup with three engineers just secured a major contract. Their secret? They used AI-enhanced workflows to explore 50 design variations in an afternoon – something that would have taken the larger team weeks. The difference isn't talent. It's approach. The engineers thriving today aren't just adopting AI tools. They're solving the knowledge problem that's been silently sabotaging innovation for decades. I recorded a deep conversation about this – covering the workflows, tools, and mindset shifts that separate 10X engineers from everyone else. https://hubs.ly/Q03q3lwF0 P.S. If you're dealing with this knowledge scattered across your team, we should talk. What we're building at Narratize is specifically designed to solve this problem. Hit me up if you want to see how other engineering teams are cutting their development cycles in half. #Engineering #Innovation #ProductDevelopment #NewProductDevelopment #manufacturing #productengineering #productengineers