Common Pitfalls In AI Predictive Maintenance Projects

🚨 Thinking of adopting AI in manufacturing? Beware these common pitfalls! While AI has incredible potential to transform manufacturing, getting started isn’t as simple as flipping a switch. Here are the first five mistakes we see too often: 1/ Skipping Data Foundation Work: Without clean, structured data, AI insights will fall flat. Solid data foundations are essential. 2/  Neglecting Data Orchestration: AI needs data from across your systems (ERP, MES, IoT) to be effective. Siloed data means siloed insights. 3/  Rushing Model Deployment: Deep learning models can be powerful, but only when rigorously tested and aligned with real use cases. Hasty deployment often leads to poor results. 4/  Overlooking Continuous Maintenance: AI isn’t “set and forget”—models need regular updating to stay accurate as your production environment evolves. 5/ Underestimating Change Management: AI adoption requires employee buy-in. Effective change management ensures teams understand and trust the new tech. These first steps make all the difference in AI success. Stay tuned for Part 2 with the next five mistakes!👇 #AI #Manufacturing #Industry40 #DigitalTransformation #SmartManufacturing #MachineLearning #DataOps #Innovation

AI projects are failing—not loudly, but quietly and often. Last week, I shared some learnings from AI initiatives we've run over the past couple of years. These were not theoretical ideas. These were real projects, built for real businesses, by real teams. Some succeeded. Some taught us what not to do. Warren Buffett: "The first rule is: don’t lose money." In the AI world, the first rule should be: don’t let the project fail. 🔁 1. Chasing AI without a real business problem This is the #1 reason AI projects fail. The excitement is real, but the clarity is missing. Too many initiatives start with, “We have to do something in AI. The Board/CEO wants it.” When you ask “Why?”—the answers get fuzzy. There’s often no alignment with a meaningful problem, no defined outcome, and no plan for business value. You must start with a sharp, urgent problem. Ask: - Is it real and recurring? - Is it costing us time, money, or customers? - Is solving it a priority for leadership? If the answer is lukewarm, drop it. Don’t chase hype—solve pain. 📉 2. No data, but big ambitions AI needs fuel—and that fuel is data. Most companies don’t even have decent dashboards, but they want AI to “think” for them. You can’t train models on instincts or opinions. AI needs history, decisions, edge cases, and volume. Before even thinking about AI, get your data stack in order: - Start capturing what matters. - Structure and cleaning it consistently. - Build visibility through dashboards. 🧠 3. Ignoring the role of context Even the best algorithms are clueless without context. What works in one scenario may totally fail in another. AI can’t figure that out on its own. Think of it like this: if I’m asked to speak at an event, I’ll want to know the audience, their challenges, the format—otherwise, I’ll miss the mark. AI is the same. Without business logic, edge conditions, and constraints, its outputs are generic at best, misleading at worst. ⚡ 4. Forgetting hidden and ongoing costs Many leaders assume AI is a one-time build. It’s not. Even after a model is trained, there’s hosting, fine-tuning, monitoring, guardrails, integrations, and more. And the infra isn’t free—especially if you’re using Gen AI APIs. Today, a lot of this cost is masked by subsidies from big players. But like every other tech cycle, the discounts won’t last. 🧭 So what should companies actually do? - Map where time and money are leaking internally. - Start capturing data in those areas—every day, every interaction. - Use dashboards and analytics before jumping to AI. - Identify where automation or decision support can create value. - Train your systems not just with data, but with your decision logic. And make sure AI is embedded where work happens—not in some separate tab. If your team needs to “go to ChatGPT”, they won’t. The AI has to come to them—right inside their workflows. 🚶♂️ Crawl → Walk → Run The hype will make you want to run. But strong AI systems are built the boring way.

Here are my Top AI Mistakes over the course of my career - and guess what thebtakeawaybis - deploying AI doesn’t guarantee transformation. Sometimes it just guarantees disappointment—faster (if these common pitfalls aren’t avoided). Over the 200+ deployments I’ve done most don’t fail because of bad models. They fail because of invisible landmines—pitfalls that only show up after launch. Here they are 👇 🔹 Strategic Insights Get Lost in Translation Pitfall: AI surfaces insights—but no one trusts them, interprets them, or acts on them. Why: Workforce mistrust OR lack of translators who can bridge business and technical understanding. 🔹 Productivity Gets Slower, Not Faster Pitfall: AI adds steps, friction, and tool-switching to workflows. Why: You automated a task without redesigning the process. 🔹 Forecasting Goes From Bad → Biased Pitfall: AI models project confidently on flawed data. Why: Lack of historical labeling, bad quality, and no human feedback loop. 🔹 The Innovation Feels Generic, Not Differentiated Pitfall: You used the same foundation model as your competitor—without any fine-tuning. Why: Prompting ≠ Strategy. Models ≠ Moats. IP-driven data creates differentiation - this is why data security is so important, so you can use the important data. 🔹 Decision-Making Slows Down Pitfall: Endless validation loops between AI output and human oversight. Why: No authorization protocols. Everyone waits for consensus. 🔹 Customer Experience Gets Worse Pitfall: AI automates responses but kills nuance and empathy. Why: Too much optimization, not enough orchestration. 👇 Drop your biggest post-deployment pitfall below ( and it’s okay to admit them - promise) #AITransformation #AIDeployment #HumanCenteredAI #DigitalExecution #FutureOfWork #AILeadership #EnterpriseAI