Jeff Winter's Recent LinkedIn Posts

ROI is one of the most powerful metrics in business… …and one of the most misapplied. It works best in stable environments with known inputs and predictable outputs. Cost reduction, throughput, scrap. That is where ROI is reliable. The issue is when it gets forced into transformation, especially when ways of working, decision-making, and capabilities are changing. Now we are asking: • What is the ROI of better decisions? • What is the ROI of clean, usable data? • What is the ROI of responding faster than competitors? That is a mismatch between the metric and the value. ROI measures financial return under relatively fixed conditions. Any type of business transformation, however, creates new conditions. Trying to quantify that too early often leads to false precision and delayed action. Some outcomes do not fit neatly into an ROI model: • Decision cycle time dropping from days to hours • Fewer disruptions due to earlier issue detection • Higher adoption of systems • New capabilities that enable future revenue So if ROI matters, treat it like a discipline. Define success upfront, track leading indicators, assign ownership, and revisit assumptions. And be clear where ROI should be the scorecard, and where it should not. 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐦𝐲 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐰𝐢𝐭𝐡 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐣𝐮𝐢𝐜𝐲 𝐬𝐭𝐚𝐭𝐬: https://lnkd.in/e3v6DNMA ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications! MX.0 Media

𝐀 𝐒𝐢𝐩 𝐨𝐟 𝐂𝐨𝐧𝐟𝐥𝐢𝐜𝐭 There’s something immediately uncomfortable about this image. Not because anything is technically “wrong”, as this is a safe drinking fountain with clean water… but because everything is contextually wrong. A drinking fountain where a toilet should be. A behavior that belongs in one setting, forced into another. A familiar action, now suddenly inappropriate. Your brain tries to reconcile it...and can’t. That tension you feel? That’s conflict of context. Now look at how we’re using AI. We drop it into environments it doesn’t understand and expect it to perform like it does. LLMs on messy data. Copilots inside undefined processes. Recommendations without constraints. It sounds intelligent. But something feels off. Because AI without context doesn’t reason, it guesses. And at scale, those “almost right” answers become real operational risk. The issue isn’t the model. It’s forcing intelligence into systems that were never structured to support it. Because intelligence needs context. Otherwise… it just doesn’t belong there. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Guess who we ran into on the streets of New York City last night? 😇 We’re walking along and something catches my eye. I stop for a second, look again, and say, “Hold on… I’m pretty sure that’s ___________” The kids immediately go from casual walking to full investigative mode. “Wait… really?” “Dad are you serious?” “Should we ask for a picture?” Next thing you know we’re saying hello and snapping a quick family photo with someone whose childhood résumé includes: • surviving multiple attempts by a very determined villain • attending a rather exclusive boarding school in Scotland • being unusually skilled with a broomstick Best part is… because I was holding the kids I had to ask him to take the selfie 🤣 New York is wild sometimes. So… who is it? ⚡️

“Houston… we have a problem.” Not with AI. With our expectations. Industrial AI is exploding in conversation, investment, and vendor announcements. According to IoT Analytics, the market already reached $𝟒𝟑.𝟔 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 in 2024 and is projected to grow to about $𝟏𝟓𝟒 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 by 2030. When most people think about AI, they picture software. Platforms. Models. Tools. Copilots. But the actual state of Industrial AI spending tells a very different story. 𝟓𝟐% 𝐨𝐟 𝐚𝐥𝐥 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐀𝐈 𝐬𝐩𝐞𝐧𝐝𝐢𝐧𝐠 𝐭𝐨𝐝𝐚𝐲 𝐠𝐨𝐞𝐬 𝐭𝐨 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 • Consulting. • Integration. • Data engineering. • Deployment. • Operationalization. In other words, more than half of the investment isn’t going into AI software at all. It’s going into 𝐦𝐚𝐤𝐢𝐧𝐠 𝐀𝐈 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞. Factories are complex environments built over decades. Systems don’t always connect cleanly. Operational data lives in dozens of places. And workflows were designed for people making decisions, not algorithms. So before AI can scale, companies have to build the plumbing. That’s why so many Industrial AI projects still start with practical applications like quality inspection, process optimization, and decision-support tools rather than full autonomy. The technology is advancing quickly. But the factory still has to catch up. I wrote a deeper breakdown of what the latest Industrial AI data and trends reveal based on the huge amount of research conducted by IoT Analytics in their 399-page 2025 Industrial AI Report. 𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e2-GJZYJ ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Yep, this was a real thing. Invented in 1925 by Hugo Gernsback, The Isolator was a bulky helmet designed to block every sound and nearly every sight, leaving only a tiny slit for reading and a tube for oxygen. It was meant to eliminate distractions so people could focus. Ironically, a century later, companies need the same thing…just for a different kind of noise. As an example, lets talk about the distractions AI is causing. AI has become as ubiquitous as the internet or electricity. It’s everywhere, in everything, and the pressure to “do something with AI” is deafening. But before you rush to implement it across every process, take a step back and decide: What role should AI actually play in your organization? Is it a core competency? Part of your value proposition? A tool for efficiency? Or a new source of differentiation? That answer determines everything: your priorities, your scope, and your attention. From there, build an AI strategy and policy so everyone knows what’s going on. In early phases, that might mean encouraging experimentation, education, and responsible adoption. But over time, AI should integrate into your overall business strategy, not live in its own silo. The goal isn’t to isolate from AI. It’s to focus it, to know where it helps you work better, faster, and smarter today, and how it can help you create and capture new value tomorrow. You don’t need a helmet to stay focused, you just need purpose and a good strategy. 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eRwaNn7B ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

In every factory, there’s a hidden clock running. A machine drifts out of tolerance. A supplier shipment slips. A quality trend begins to form. Demand signals start shifting. The clock starts the moment the event occurs. But the organization doesn’t react immediately. First someone has to 𝐧𝐨𝐭𝐢𝐜𝐞 𝐢𝐭. Then someone has to 𝐟𝐢𝐠𝐮𝐫𝐞 𝐨𝐮𝐭 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐦𝐞𝐚𝐧𝐬. Then someone has to 𝐝𝐞𝐜𝐢𝐝𝐞 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨. Then someone has to 𝐞𝐱𝐞𝐜𝐮𝐭𝐞 𝐭𝐡𝐞 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞. That gap is what I call 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐥𝐚𝐭𝐞𝐧𝐜𝐲. The research is clear: speed fuels profitability. Companies that make decisions 30% faster see 16% higher profit margins than those that don’t. The difference between leaders and laggards isn’t access to data, it’s how quickly they turn that data into impact. And it shows up in four very predictable stages: • 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 – how long it takes to realize something happened • 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 – how long it takes to understand it • 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 – how long it takes to choose a response • 𝐀𝐜𝐭𝐢𝐨𝐧 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 – how long it takes to actually do something about it Most factories still operate with hours, days, or even weeks across those four steps. Industry 4.0 compresses them. 𝐑𝐞𝐚𝐝 𝐦𝐨𝐫𝐞: https://lnkd.in/ec8Vra-d ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

My keynote from ProveIt! Conference just dropped. Now you can watch it 😀 This keynote is about a problem most manufacturers don’t explicitly see: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐋𝐚𝐭𝐞𝐧𝐜𝐲. Not a lack of data. Not a lack of AI. But the gap between knowing something and actually doing something about it. I start with the explosion of data and how it’s outpaced our ability to coordinate decisions across systems and teams. Then I reframe AI not as a tool, but as a spectrum of capability: • 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞: how well systems understand • 𝐀𝐠𝐞𝐧𝐜𝐲:  how much they can act Because the goal isn’t to deploy AI, it’s to improve how decisions are made and executed. And that’s where most companies stall. So it comes down to three things to move from pilot to scale: • 𝐀𝐧𝐜𝐡𝐨𝐫 𝐢𝐭:ground AI in real operational data • 𝐀𝐮𝐭𝐡𝐨𝐫𝐢𝐳𝐞 𝐢𝐭: define decision ownership and boundaries • 𝐀𝐝𝐚𝐩𝐭 𝐢𝐭: build a system that continuously learns and improves Because an intelligent factory isn’t a system upgrade. It’s a redesign of how decisions happen. Now, picture me saying this as Steve Jobs…“One more thing…” A full downloadable PDF is included. And yes, every stat in the presentation is cited with a link to dive deeper. How many other presentations provide that? You're welcome 😉 𝐊𝐞𝐲𝐧𝐨𝐭𝐞:

Step 1 in going paperless: print 12 copies of the plan. 😎 There’s a strange comfort in it. If it’s printed, reviewed, and passed around, it feels real. It feels finished. It feels like progress. That instinct doesn’t disappear just because the tools change. It follows transformation right into the digital world. Paper thinking is what turns a live dashboard into a PDF before the meeting. It’s what recreates multi-step approval chains inside workflows that were supposed to remove them. It’s exporting data into Excel to “clean it up,” even when the system already shows the answer in real time. It’s holding meetings to align on information that everyone can already see. The technology changes. The behavior doesn’t. Digital isn’t meant to replicate paper. It’s meant to replace the need for it entirely. Digital systems are dynamic, shared, and continuously evolving. They’re designed for decisions to happen closer to the data, not after it’s been packaged, distributed, and reviewed. But paper thinking pulls everything back toward what feels familiar. It introduces delays where speed was possible, adds structure where flexibility was needed, and preserves control where autonomy should exist. So the real question isn’t whether paper has been eliminated. It’s whether the mindset behind it has. Because a paperless organization with paper thinking isn’t transformed. It’s just doing the same work… with better formatting. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

We’re starting to hear Industry 5.0 pop up more and more. In fact, according to Google Trends, global interest hit an all-time high during October of 2025. (For comparison, it is about 20% of the volume of ‘Industry 4.0.’) But here’s the real question: Is the term gaining traction because people love it... or because they’re debating it? It seems like everyone has an opinion. Some see it as the natural next step, focusing on human-centricity, sustainability, and resilience. Others argue it’s simply a continuation of Industry 4.0’s evolution, making the name more confusing than helpful. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈’𝐦 𝐜𝐮𝐫𝐢𝐨𝐮𝐬 𝐚𝐛𝐨𝐮𝐭: • Do you think ‘Industry 5.0’ is a necessary distinction, or are we just renaming concepts already embedded in Industry 4.0? • Is it helpful to define new phases of industrial progress, or does it risk creating unnecessary fragmentation? Want to learn more about this concept, how they compare, and what people think about it? (including myself 😎 ). 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:  https://lnkd.in/ebMYQMjE Whether you love the term, hate it, or are just hearing it for the first time, I’d love to hear what you think. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Step 1: Announce transformation. Step 2: Buy software. Step 3: Wonder why nothing changed. Somewhere between Step 2 and reality… things get interesting. Because transformation doesn’t happen when the system goes live. It happens when people start working differently, making different decisions, and letting go of the old ways that used to feel safe. That part does not follow a clean timeline, even if the project plan says it should. What actually unfolds is a very human progression that shows up in almost every transformation effort: 𝟏) 𝐑𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 – People aren’t blocking change. They’re protecting themselves from what it might mean. 𝟐) 𝐂𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧 – The why is clear to leadership, but the what and how are fuzzy to everyone else. 𝟑) 𝐅𝐫𝐮𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 – New tools make work harder, shadow systems return, and people start asking, “Was this worth it?” 𝟒) 𝐏𝐚𝐧𝐢𝐜 – Reality breaks the plan. Priorities spin. Truth surfaces. This is where leadership is tested. 𝟓) 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡 – Not a celebration, but a quiet moment when someone says, “This actually works.” Momentum finally kicks in. Most organizations spend far more time in the middle three than they expect. That is where side systems reappear, workarounds multiply, and people begin to question whether the effort was worth it. But that middle is not failure. It is the work. It is where strategy meets behavior, where culture gets tested, and where leadership either reinforces the change or unintentionally undermines it. If everything feels smooth, you are likely implementing, not transforming. Real transformation creates friction before it creates momentum. The companies that succeed are the ones that recognize these stages for what they are and lead through them, rather than trying to avoid them. If you are in that messy middle, you are not behind. You are exactly where transformation becomes real. 𝐑𝐞𝐚𝐝 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐬𝐭𝐚𝐠𝐞𝐬 𝐚𝐧𝐝 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: https://lnkd.in/e34k7Ak4 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Some say “Industry 4.0” is old news. To those, I say… explain this chart. 😇 This is Google Trends showing relative global search interest (not total volume), comparing two search terms: Industry 4.0 and Smart Manufacturing. I started it in 2010, right before 'Industry 4.0' really became a term, to capture the full story. And if you follow the curve… mid-2025 jumps out. Here’s what I see: 𝟏) 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝟒.𝟎 𝐢𝐬 𝐡𝐨𝐭𝐭𝐞𝐫 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫.  Mid-2025, searches spike. Not just I4.0, but AI, Digital Transformation, all of it. I’d argue AI is the trigger. Most Industry 4.0 maturity models didn’t say “AI is the end state,” but the top levels (autonomy, adaptability, self-optimizing systems, etc.) were never achievable without it. Now that AI is real, accessible, and actually working… those upper levels feel within reach. So people are going back, reconnecting the dots, and asking, how do we actually get there? 𝟐) 𝐒𝐦𝐚𝐫𝐭 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐣𝐮𝐬𝐭 𝐭𝐨𝐨𝐤 𝐭𝐡𝐞 𝐥𝐞𝐚𝐝 This is the more interesting shift. “Industry 4.0” didn’t lose. It got narrowed… and operationalized. Smart Manufacturing is the practical slice of Industry 4.0. More focused. More defined. Less philosophical. Fewer debates about what it means, more agreement on what to do. • Ask 10 people what 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝟒.𝟎 is, you’ll get 12 answers. • Ask them about 𝐒𝐦𝐚𝐫𝐭 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠, you’ll get something closer to a roadmap. That’s why it wins in search. It’s not broader. It’s clearer. So what do you think caused this? 😀 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Everyone is sprinting into AI right now. And that makes sense. The technology is moving fast, the pressure is real, and nobody wants to be the company that waited too long. At the same time, AI is new territory for everyone. Some companies are experimenting with early use cases. Some are trying to scale pilots into real operational value. Others are still figuring out where AI actually fits in their business. And the hardest part of the journey doesn’t stay the same for long. The biggest challenge this year might not be the biggest challenge next year. In some organizations it changes month to month. One month the struggle is defining 𝐰𝐡𝐚𝐭 𝐀𝐈 𝐫𝐞𝐚𝐥𝐥𝐲 𝐦𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬. The next month it’s 𝐡𝐨𝐰 𝐭𝐨 𝐞𝐱𝐞𝐜𝐮𝐭𝐞. Then it becomes 𝐰𝐡𝐢𝐜𝐡 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 𝐭𝐨 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞. Or 𝐰𝐡𝐨 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐨𝐰𝐧𝐬 𝐢𝐭. So consider this a quick pulse check. Where are you struggling the most right now in your AI journey? Vote in the poll. 👇 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Most companies today have more data than they know what to do with. And yet, somehow, they still struggle to answer the simplest question: 𝐖𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨 𝐧𝐞𝐱𝐭? For nearly a century, one simple model has explained how intelligence develops. It’s called the 𝐃𝐈𝐊𝐖 𝐏𝐲𝐫𝐚𝐦𝐢𝐝: 𝐃𝐚𝐭𝐚 → 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 → 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 → 𝐖𝐢𝐬𝐝𝐨𝐦. The framework dates back to the 1930s and was later expanded by thinkers like Russell Ackoff. Long before AI, it explained how raw facts become meaningful decisions. And despite all the modern technology we talk about, most organizations are still climbing this same pyramid. The problem is many companies never make it very far... 𝐃𝐚𝐭𝐚 - Raw facts and observations with no inherent meaning. Data answers nothing on its own. It’s just the raw material that needs structure and context. 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 - Data organized with context so it becomes useful. Information answers “what happened?”, but it still doesn’t explain why. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 - Information analyzed to reveal patterns, cause and effect, and deeper understanding. This is where we begin answering “how?” and “why?” 𝐖𝐢𝐬𝐝𝐨𝐦 - Applying knowledge to make decisions and guide future action. This is where insight turns into judgment about what matters and what should happen next. In the age of AI, this pyramid matters more than ever. AI can process data faster than humans. It can surface patterns and generate insights. But moving from knowledge to wisdom still requires context, judgment, and priorities. Technology can help us climb the pyramid faster. But it still doesn’t decide what matters. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐝𝐢𝐯𝐞 𝐝𝐞𝐞𝐩𝐞𝐫 𝐢𝐧𝐭𝐨 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨 𝐢𝐧 𝐞𝐚𝐜𝐡 𝐬𝐭𝐚𝐠𝐞?  https://lnkd.in/eg8H3j5w ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Forgive me, CIO, for I have sinned… I’ve witnessed the 𝐒𝐞𝐯𝐞𝐧 𝐃𝐞𝐚𝐝𝐥𝐲 𝐒𝐢𝐧𝐬 of Digital Transformation more times than I can count. Different companies. Different technologies. Same mistakes. 𝐅𝐨𝐜𝐮𝐬𝐢𝐧𝐠 𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐨𝐯𝐞𝐫 𝐩𝐞𝐨𝐩𝐥𝐞: While technology is certainly an important aspect of digital transformation, it’s arguably more important to consider how the changes will impact employees. Strong leadership and good change management will help ensure employees are inspired, motivated, and aligned with the digital strategy. 𝐍𝐞𝐠𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: Customer experience must be at the heart of digital transformation. Process optimization and cost reduction will help improve the business, but they won’t change the game. Leveraging high-quality product, customer, and market data allows companies to create new business models that provide better product/service performance, customer experience, and business outcomes for customers. 𝐃𝐨𝐢𝐧𝐠 𝐢𝐭 𝐚𝐥𝐨𝐧𝐞: Adopting and implementing a variety of new technologies that need to cohesively work together while simultaneously modifying processes and managing cultural changes is a tough undertaking. Very few companies, if any, have the expertise and experience in-house to pull it off. Partner with experts you trust! 𝐍𝐨𝐭 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐢𝐧𝐠 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲: As organizations adopt new technologies, it's important to consider the security implications, both for the organization and for the customers. Cybersecurity is too important to be considered an afterthought in the digital transformation process. 𝐍𝐨𝐭 𝐛𝐞𝐢𝐧𝐠 𝐚𝐠𝐢𝐥𝐞: Very few large-scale transformations go exactly as planned. In addition, technological advancements and market conditions are changing so fast that failure to react and adapt quickly can be catastrophic. Embrace digital and cultural agility to foster an environment of continual innovation. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐚 𝐜𝐥𝐞𝐚𝐫 𝐯𝐢𝐬𝐢𝐨𝐧 & 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲: A good digital transformation strategy should describe what success looks like, how it will be graded, and how the combination of technologies will be integrated across all systems and processes to transform the business. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐚𝐠𝐫𝐞𝐞𝐦𝐞𝐧𝐭 & 𝐜𝐨𝐦𝐦𝐢𝐭𝐦𝐞𝐧𝐭: Digital transformations are designed to be disruptive and can often be costly. Company leaders not only need to agree on the digital strategy, but they also must be fully committed. Otherwise, digital initiatives will run the risk of goal and priority misalignment, high levels of resistance, and loss of funding. Commit one and you learn a lesson. Commit all seven and you repeat the same program in three years. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e4pP2B6Z

AI is not confusing. Your organization is. This report makes that painfully obvious. I was interviewed for a new PEX Network report on AI-driven business transformation, where I shared my perspective on scaling beyond pilots, measuring ROI, and what governance actually looks like once AI starts influencing real decisions. At the time, I knew my answers. What I hadn’t seen yet was how everything came together across the full report. Now I have. It’s 14 pages. And it’s not what you’d expect. Not a market report. Not a how-to guide. Not another polished AI playbook. It’s a set of perspectives that, together, start to reveal what’s actually going on inside companies right now. My contribution centered on a few things I see constantly. Pilots prove the tech works, not that the business is ready. AI only creates value when it changes real decisions. And once systems start acting, governance becomes a question of ownership. Then you step back and look at the whole report... Every company says AI is critical. Very few behave like it. Different contributors. Same friction points. Data. Process. Ownership. Also, working with Amelia Brand on this was a great experience! The report is sharp, readable, and grounded in reality. If AI feels harder than it should right now, this will likely explain why. 𝐂𝐡𝐞𝐜𝐤 𝐢𝐭 𝐨𝐮𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/esNsZVEx Was your takeaway the same as mine? ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Who’s going to Hannover Messe this year? 😀 Because I’ll make it easy…If you’re looking for me, I won’t be hard to find. I’m heading to Hannover specifically to be at the Critical Manufacturing booth. Call it a reunion. Call it unfinished business. Call it someone who still believes a little too strongly in making Industry 4.0 actually real. 😎 I’m in an advisor role now, but not the kind where you disappear into the background. I’ll be right there in the booth, talking with anyone willing to get into the details of what this all actually looks like when it works… and when it doesn’t. So if you’re at Hannover on Tuesday: Come by 𝐇𝐚𝐥𝐥 𝟏𝟓, 𝐁𝐨𝐨𝐭𝐡 𝐂-𝟏𝟒 • Say hi. • Ask something real • Or just see if I live up to whatever you expected from my posts 😇 See you there! ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

“Digital transformation” has become the corporate version of “getting in shape.” Everyone says it. Few define it. Transformation isn’t one thing. It’s six very different bets. • You can redesign how work gets done. • You can reinvent how you make money. • You can reshape culture. • You can elevate customer experience. • You can build ecosystems. • You can even enter entirely new domains. Each one requires different investments, leaders, timelines, and risk tolerance. Yet many companies try to bundle them into one massive initiative… …and wonder why nothing really sticks. You should absolutely consider all six types of digital business transformations: 𝐏𝐫𝐨𝐜𝐞𝐬𝐬. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥. 𝐂𝐮𝐥𝐭𝐮𝐫𝐞. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦. 𝐃𝐨𝐦𝐚𝐢𝐧. They are interconnected whether you like it or not. But you don’t attack all six at once. You prioritize. You sequence. You focus. Otherwise, you end up with a little bit of progress everywhere… and no meaningful change anywhere. Transformation isn’t about doing more. It’s about doing the right things, in the right order, for the right reason. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

You laugh at the example… using your phone’s flashlight to find your phone. But then you go to work and do the same thing at scale. Using one system to figure out another. Using another tool to explain both. The illusion of clarity created by an overload of information. Everything is visible. Nothing is obvious. More technology isn’t the answer. Better structure is. Here are three pieces of advice for businesses to avoid falling into the Digital Daze: 𝟏. 𝐇𝐮𝐦𝐚𝐧-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Always put your people first. Technology is a tool, not a replacement for human insight and creativity. Train your teams not just to use new tools but to understand their purpose and potential impact on the business. Human intuition and emotional intelligence are irreplaceable assets in making the most of technology. 𝟐. 𝐒𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 𝐢𝐬 𝐊𝐞𝐲: In our pursuit of the latest and greatest tech, we often forget the beauty of simplicity. Adopt technologies that simplify tasks, not complicate them. The goal is to enhance productivity and clarity, not to add layers of digital complexity that can lead to confusion and inefficiency. 𝟑. 𝐑𝐞𝐠𝐮𝐥𝐚𝐫 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐃𝐞𝐭𝐨𝐱𝐞𝐬: Just as individuals need breaks from screens, organizations need periodic 'detoxes' from their heavy tech-dependence. Schedule times to step back and evaluate. Are these tools serving their purpose? Can processes be streamlined? Sometimes, stepping away gives a clearer view of what’s truly essential. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eSigc4GF What is your worst (or funniest) Digital Daze moment? ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

🎶 Guess who’s back… back again… 🎶 Yep. For the 6th time, I’ll be returning as Chair and Host of MX.0 Southeast this April in Greenville, SC. 😎 🎶 Jeff is back… tell a friend… 🎶 This is one of my favorite events because it hits a rare balance. Big enough to bring together leaders from companies like GE Aerospace, Michelin, Jabil, Medline, Eaton, and others… but small enough that you actually meet people instead of just passing them in hallways. 🎶 Now this looks like a job for me… 🎶 I’ll be moderating two panels while we tackle topics like AI in industrial operations, scaling digital initiatives, and one of my favorite subjects: 𝐈𝐓/𝐎𝐓 𝐜𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞, which remains one of the most fascinating (and stubborn) challenges in modern manufacturing 🎶 So everybody just follow me… 🎶 There will be interactive discussions, problem-solving tables, demo tours, and yes… a happy hour networking event at the end of the day. 😀 🎶 Cause we need a little… manufacturing strategy… 🎶 Hope to see a lot of you in Greenville. 📍 MX.0 Southeast 📅 April 7–8, 2026 📌 Greenville, South Carolina 𝐌𝐨𝐫𝐞 𝐢𝐧𝐟𝐨 𝐚𝐧𝐝 𝐫𝐞𝐠𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: https://lnkd.in/eFDeBxhi ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

A few years of nerdy manufacturing conversations just turned into something official. I’m excited to share that I’ve joined IndustrialMind.ai as an Executive Advisor. 😀 This partnership actually started several years ago in conversations with Steven Gao. Steven helped build manufacturing AI systems during the ramp of Tesla’s Shanghai Gigafactory, where the challenge was not collecting more data. The challenge was helping engineers make better decisions faster during some of the most intense production ramps in the world. Those conversations resonated with me immediately because that is exactly the world I live in. Manufacturing does not struggle with tools. It struggles with decision speed and engineering throughput. IndustrialMind.ai is building what they call an “AI Engineer”. The system reads engineering drawings, generates process plans, monitors production behavior, and helps engineers accelerate root cause analysis when something goes sideways. In short, it focuses on the daily work of engineering and operations teams. I’ll be working with Steven Gao, Justin Li, and the team on strategy and industry perspective as they scale. It also keeps my foot firmly in the AI-driven manufacturing engineering world, which nicely complements the work I’m doing around industrial infrastructure, IT/OT Convergence, and Physical AI at Belden Inc.. 𝐅𝐮𝐥𝐥 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/efutTFba ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

This is the single most important graphic for Industry 4.0 you will ever see. Still loading? Don’t worry. It usually takes companies 3–5 years to see it too. 🤣 Somewhere inside every organization there is a diagram that explains: • where operational data originates • how it flows across machines, systems, and applications • where context gets added • who actually owns the decisions • where analytics and AI create value That diagram is the difference between a factory that experiments with technology and one that actually transforms. The problem is that most companies never quite manage to draw it. Ask the OT team and you’ll get a diagram full of PLCs, historians, SCADA systems, and machine connectivity. Ask the IT team and you’ll see ERP, cloud platforms, data lakes, APIs, and security layers. Ask the data team and suddenly the picture is full of pipelines, models, and dashboards. None of these diagrams are wrong. They’re just incomplete. So what happens next is predictable: Companies add more tools. More platforms. More layers. The architecture diagram grows… but clarity doesn’t. Industry 4.0 isn’t missing technology. Sensors, connectivity, cloud platforms, and AI are advancing faster than ever. What most organizations are missing is 𝐜𝐥𝐚𝐫𝐢𝐭𝐲. Clarity about how the system works. Clarity about how data flows. Clarity about where decisions actually happen. When that clarity is missing, transformation turns into disconnected projects instead of a coherent system. MES initiatives. IoT pilots. AI experiments. All promising progress. All adding complexity. Industry 4.0 doesn’t stall because the technology is immature. It stalls because the architecture never fully loads. If leaders cannot explain how their operational data moves from machine → system → decision, the architecture is not yet ready. And until the architecture is clear, the transformation will keep buffering. 😎 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞:  https://lnkd.in/ei-ZqUr5 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

For all the talk about Industry 4.0 and AI, there’s a simple question most manufacturers still want answered: What are other manufacturers actually doing? Last year at Critical Manufacturing’s MES & Industry 4.0 Summit, we tried to answer that question. Before the workshop, the manufacturers attending had to do something they weren't used to: Homework! The entry fee was to complete a pretty extensive survey: Strategy, leadership ownership, data architecture, AI use cases, priorities for the year, biggest obstacles… a lot of detail. We reviewed 94 different Industry 4.0 and AI use cases across 9 functional domains, looking at which ones manufacturers currently have in production and which ones are still in pilot phases. Then we aggregated everything and shared the results back with the room. And that turned into some of the most interesting conversations of the event. People could finally see where they stood relative to their peers and where the industry really is right now. I think you’ll be surprised by which use cases had the highest adoption. And just as interesting, which ones had zero adoption at all. 😬 𝐘𝐨𝐮 𝐜𝐚𝐧 𝐝𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐢𝐬 𝟐𝟓-𝐩𝐚𝐠𝐞 𝐞𝐛𝐨𝐨𝐤 𝐡𝐞𝐫𝐞: https://lnkd.in/ezH-kY2q A few things that stood out to me when reviewing the results: • About half of manufacturers still don’t have a formal Industry 4.0 strategy, even though most are investing in digital initiatives. • IT and OT alignment is still a work in progress across much of the industry. • Despite the attention around AI, very few companies are using it deeply in production operations today. What most companies are focused on instead is building the foundation: MES, machine connectivity, and getting data organized so it can actually be used. One thing became clear during the workshop: Even the most advanced companies in the room were still only using a small portion of the possible Industry 4.0 capabilities. As a Strategic Advisor to Critical Manufacturing, I’m glad these insights are now available for others in the industry to explore. 😀 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Few environments are harder to manage than manufacturing. At any given moment, performance is being shaped by machine conditions, operator variability, material availability, quality outcomes, maintenance events, and demand signals. Studies show manufacturers regularly track hundreds of KPIs across these dimensions, but only a small subset actually drives decisions. The difficulty is not lack of data, it is navigating an overwhelming number of variables that all influence each other. There is no limit to what you can measure. But performance doesn’t happen in a list… it happens in a flow. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 → 𝐀𝐜𝐭𝐢𝐯𝐢𝐭𝐢𝐞𝐬 → 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 → 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 → 𝐈𝐦𝐩𝐚𝐜𝐭. Most KPI sets ignore that progression. They mix everything together (leading and lagging, operational and financial) and expect clarity to emerge. It doesn’t. The value comes from understanding where a metric sits in that system… and how it connects to the ones before and after it. That’s when KPIs stop being numbers… and start becoming a model of how your business actually runs. Which KPI do you trust the most when making a tough call? 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐥𝐢𝐧𝐤𝐞𝐝 𝐛𝐞𝐥𝐨𝐰, 𝐰𝐡𝐞𝐫𝐞 𝐈 𝐝𝐢𝐬𝐜𝐮𝐬𝐬: • ISO 22400-2 for manufacturing operations KPIs • Smart factory KPI statistics • Methods for performance evaluation and monitoring • The stages of performance evaluation • The distinctions between diffusion, adoption, and performance KPIs 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eN_GKCmG ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I recently walked to the edge of the cliffs at Old Head in Kinsale, Ireland. Unfortunately, I saw nothing. Just fog. 😕 No horizon. No waves. No sense of scale. I knew there was an ocean out there… somewhere. But knowing isn’t seeing. So I guessed. I imagined what it should look like. I filled in the gaps and built a version of reality in my head that felt right. Then I waved my hands through the fog (with a little help from AI 🤣 )...and everything changed. The cliffs dropped. The ocean stretched forever. It was beautiful!!! What I thought It was supposed to look like… wasn’t even close. That’s operations without visibility. You know there’s a problem (or maybe an opportunity). You assume the line is running fine. You believe inventory is where it should be. You trust the plan is being followed. Not because you see it…but because you’ve built a mental model that feels right. And to be fair…a lot of those guesses are decent. But they’re still guesses. In my case, the fog shaped my perception. In operations, it shapes decisions. Same problem. Different consequences. Here’s the difference: At the cliffs, I couldn’t remove the fog. In operations… you can. It’s the difference between reacting to reality…and reacting to a story your brain made up. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

146 researchers just published the results of a 2-year, multi-university study... and it challenges the entire foundation of modern AI.😬 They didn’t make models faster. They didn’t make them smarter. They removed the need for decisions altogether. Inside what they’re calling the 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐬𝐭𝐚𝐜𝐤, signals don’t move linearly. They refract. Context folds into itself, and intent diffuses across layers before it’s ever observed. What looks like execution is often just a system arriving at a state it already resolved upstream. 𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐩𝐚𝐫𝐭 𝐭𝐡𝐚𝐭 𝐛𝐫𝐞𝐚𝐤𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠: Decisions no longer exist as events. They exist as probability gradients that collapse under aligned context. Agents don’t coordinate. They 'cohere' across shifting meaning fields that never fully stabilize. At scale, the system doesn’t predict the future. It slightly precedes it. Inputs arrive after outcomes have already begun forming. So the real breakthrough isn’t better AI. It’s AI that quietly makes decisions… obsolete. Sound absurd? Only if you still think decisions are required. 𝐈 𝐡𝐢𝐠𝐡𝐥𝐲 𝐞𝐧𝐜𝐨𝐮𝐫𝐚𝐠𝐞 𝐲𝐨𝐮 𝐭𝐨 𝐫𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐬𝐭𝐮𝐝𝐲:  https://lnkd.in/eHtTGn-m Did this blow your mind as much as it did mine? 😮 Remeber today’s date 😇 ————————————