Unlocking Business Value with Industrial AI

I believe AI creates real value when it tackles hard, physical problems — the kind that live in factories, warehouses, and service tasks. Recently, I learned the attached from a plastics machine manufacturer and logistics provider struggling with unpredictable production schedules, warehouse congestion, and reactive maintenance routines. When a structured AI implementation approach was brought into the equation the following outcome was achieved 👇 🔹 Smart Production Planning – Machine learning models forecasted demand and optimized resin batch production, cutting material waste by 18%. 🔹 AI-Driven Warehouse Logistics – Intelligent slotting and routing algorithms boosted order fulfillment rates by 25%, reducing forklift travel time and idle inventory. 🔹 Predictive Maintenance for Service Teams – Sensor data and pattern recognition flagged early signs of machine wear, reducing unplanned downtime by 30%. The result wasn’t automation replacing people — it was augmentation empowering people. Operators, warehouse managers, and service engineers gained real-time insights to make faster, better decisions. 💡 Takeaway: AI success in industrial environments isn’t about technology first — it’s about aligning data, people, and process to create measurable operational impact. #AI #IndustrialServices #SmartManufacturing #WarehouseOptimization #PredictiveMaintenance #DigitalTransformation #OperationalExcellence

Digital Twins and Industrial AI Triggered by recent keynotes, one thing is clear: Digital Twins combined with Industrial AI have crossed a decisive threshold. They are no longer innovation theatre or isolated pilots. They are becoming a foundational capability for how industrial companies operate, compete, and transform. For manufacturing and automotive companies with complex global production networks, this shift is not optional. Digital Twins are emerging as core levers for cost reduction, resilience, and speed—directly impacting margins, competitiveness, and risk exposure. The real power of Digital Twins lies not in visualization, but in their combination with AI-driven simulation, prediction, and optimization. When products, production systems, and processes are digitally represented and continuously enriched with operational data, companies can test decisions before they hit the factory floor. Virtual commissioning, simulated layout and volume changes, and predictive maintenance reduce ramp-up time, downtime, inventory, and operational firefighting. In capital-intensive industries with tight margins, this is not incremental improvement it is structural cost reduction and risk avoidance.   Manufacturing combines extreme complexity with relentless efficiency pressure. Product variants grow, software content explodes, regulatory demands tighten, and supply chains remain fragile while customers expect flawless quality at competitive cost. Digital Twins and Industrial AI enable a closed feedback loop between engineering, production, and operations: the so-called Digital Thread. Decisions move from siloed optimization to a shared, continuously updated model of reality. Companies that master this gain speed without losing control.   Digital Twins are not another tool rollout; they are an enterprise capability spanning Engineering IT, Production IT, OT, and Data & AI. The main bottleneck is rarely technology it is data. Fragmented models, inconsistent semantics, and poor data quality across PLM, MES, ERP, and the shop floor limit value creation. Without a solid data foundation, even advanced AI remains theoretical. As Digital Twins increasingly represent intellectual property and operational know-how, architecture, governance, and security become critical.   Large-scale industrial transformation is not just a technology or talent race. It is about judgement, prioritization, and execution discipline. These initiatives touch the core of the business: assets, safety, quality, cost, and risk. They require leaders who can balance speed with stability and innovation with operational continuity. This is where experience becomes a competitive advantage.   Digital Twins and Industrial AI will shape industrial operations over the next decade. This is redefining IT from technology delivery to orchestrating industrial value creation across engineering, manufacturing, and operations, while managing cyber and operational risk.

🧠 𝗙𝗿𝗼𝗺 𝗔𝗜 𝘁𝗼 𝗥𝗢𝗜: 𝗪𝗵𝘆 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗶𝗹𝗹 𝗪𝗶𝗻 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 💡 AI agents on their own rarely deliver enterprise value. The magic happens when they are not just smart, but deeply embedded into the very workflows they're meant to improve—powered by data, aligned with domain logic, and orchestrated for specific business outcomes. At my last startup, we learned this firsthand. We developed a highly accurate AI model to grade almond defects—a truly powerful piece of tech. But the real ROI didn't kick in until we "agentified" the process: → Automated object detection to identify issues. → Validation against USDA specifications for compliance. → Automated report generation to save time. → Human-in-the-loop exception handling for complex cases. That's when we shifted from a clever model to a production-grade solution that delivered a measurable return on investment. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗨𝗻𝗹𝗼𝗰𝗸 𝗳𝗼𝗿 𝗔𝗜 🔍 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘂𝗻𝗹𝗼𝗰𝗸 𝗶𝘀𝗻'𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗼𝗿 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗶𝘁𝘀𝗲𝗹𝗳; 𝗶𝘁'𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 𝘄𝗵𝗲𝗿𝗲 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝘁. That's the crucial difference between simple automation and true business transformation. The building blocks are here—foundation models, advanced reasoning, new tools. The real frontier is the application layer, where vertical agents turn that potential into profit by tackling specific, high-value workflows. 𝗪𝗵𝗮𝘁 𝗪𝗶𝗻𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 𝗟𝗼𝗼𝗸 𝗟𝗶𝗸𝗲 To drive meaningful change, your AI agents must have a deep understanding of: 1️⃣ The Workflow: They need to be embedded seamlessly into the processes they are meant to optimize. 2️⃣ The Data: They must have access to and understand the context of the data they operate on. 3️⃣ The Domain Logic: They need to execute tasks based on the specific rules and knowledge of your industry. This is how we move from simply generating outputs to delivering high-value, transformative outcomes. 𝗬𝗼𝘂𝗿 "𝗔𝗹𝗺𝗼𝗻𝗱 𝗖𝗼𝘂𝗻𝘁𝗶𝗻𝗴" 𝗠𝗼𝗺𝗲𝗻𝘁 Every organization has its own version of "almond counting"—those manual, error-prone bottlenecks that slow down progress. Think about: • Procurement and contract management • HR on-boarding and credentialing • Insurance claims processing • Manufacturing QA and defect tracking These are the prime opportunities for vertical agents to automate, orchestrate, and create a real competitive advantage. 𝗧𝗵𝗲 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗢𝗜 It's simpler than you think: 📊 𝗔𝗜 + 𝗗𝗮𝘁𝗮 + 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 + 𝗗𝗼𝗺𝗮𝗶𝗻 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 = 𝗥𝗢𝗜 𝗪𝗵𝗮𝘁'𝘀 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗠𝗼𝘃𝗲? Think about one slow, manual workflow in your organization that is still waiting for its "agent." That's your opportunity. Share it in the comments below! 👇 #ArtificialIntelligence #AI #DigitalTransformation #BusinessStrategy #Innovation #TechLeadership #FutureOfWork #VerticalAI #AgenticAI

A little over 3 years ago, I wrote in Smart Industry from Endeavor Business Media about the urgent need to rebalance the world's industrial ecosystem, shifting from centralized, labor‑dependent mega‑factories to a more distributed, digitally enabled manufacturing footprint. This recent piece from The Economist makes it clear: that inflection point has arrived, and it is actively reshaping value creation across industries. What Still Holds True 🔁 Distributed manufacturing = #resilience + margin protection. The strategic logic hasn’t changed: customer proximity, production flexibility, and ecosystem partnerships still drive outperformance. 🤖 Automation + software remains the unlock. The future isn’t about robots alone; it’s about building integrated, #reprogrammable automation systems that can be redeployed when and where needed, and scale intelligently. What’s Changed and Why it Matters 🎛️ AI has moved from optimization to #orchestration. In 2022, the conversation still centered on topics like line efficiency, yield improvement, and quality control. Today, AI is able to redesign assembly processes, dynamically adjust workflows, and balance labor, materials, and machine availability in real time. 🧠 #GenAI is closing the "sim‑to‑real" gap. AI models trained on massive sensor and vision datasets are now able to generate much more accurate #simulations, making it possible for robots to perceive, understand, and react to real‑world variability.  🌍 Global footprint strategy is being rewritten. Labor arbitrage is no longer the dominant variable; AI‑enabled productivity is. This changes where assets should sit and how they should scale. ⚡ The adoption curve has collapsed. What was once a 5 to 10-year out horizon is now a near‑term strategic imperative. Leading manufacturing companies are no longer experimenting, they are actively deploying. Even Jensen Huang has declared that "the #ChatGPT moment for robotics is here"! For executives, investors, and boards, the takeaway is simple: AI isn’t a bolt‑on to your manufacturing strategy. It’s a competitive, #system‑level capability that will separate tomorrow’s winners from the laggards. The companies that rethink their operating models now will be the ones who define and capture the next decade of industrial value creation. #PhysicalAI #FactoryoftheFuture #SmartFactories #IndustrialAutomation #AdvancedManufacturing #DistributedManufacturing Link to Smart Industry article below in the comments. https://lnkd.in/eFrArdGe

The durable value in industrial AI does not only come from training a generic industrial foundation model with industrial data. It comes from owning the workflow, the physics, and the installed base. Foundational models are an enabler, not the moat. In this paper, the authors argue that industrial AI performance and deployability depend more on system design than on model scale. The paper formalizes industrial AI as the integration of Knowledge, Data, and Model modules. In a rotating machinery fault diagnosis case study, this structured approach achieves >99% classification accuracy, compared to materially lower performance when domain knowledge and data engineering are omitted. Critically, the gain comes not from larger models, but from physics-informed feature construction, signal preprocessing, and domain constraints embedded upstream. The authors also show that over 70% of industrial AI effort lies outside model training, in data preparation, knowledge formalization, and workflow integration. #IndustrialAI #DigitalTwin #EngineeringAI #PhysicsInformedAI #PredictiveMaintenance #ManufacturingAI #TrustworthyAI #SystemsEngineering #Siemens

There’s a class of decisions in heavy industry that we still make the hard way. In a few years, we’ll see that it didn’t have to be. I’m talking about minute-by-minute operational decisions. Dozens of inputs. Multiple outputs. The relationship between cause and effect is complex. And even small improvements can unlock meaningful value, while missteps quietly erode performance, efficiency, and margin. These are exactly the kinds of problems where AI should thrive. And many have tried. Advisory systems with real-time data streams, complex models, and millions of optimization runs. But most end up ignored. Why? Because they don’t behave the way good operators do. They recommend actions that feel risky, violate operational norms, or simply don’t work in context. They optimize on paper, but not in practice. At Hatch, we’ve taken a different path. We call it AI-Based Process Control (AIPC). We start with models built by process experts, trained on live plant data. Then we surround those models with real guardrails: pre- and post-processing layers that codify engineering logic and operator judgment. These layers apply the same heuristics, constraints, and stabilizing actions that experienced operators rely on. The difference is that they’re implemented in a way the system can execute, validate, and adapt to in real time. This is what lets us close the loop. We move from insight to action, and from action to impact. Across concentrators, furnaces, autoclaves, and tailings systems, the pattern is often the same. Either these assets are underperforming, or they’ve adopted AI advisory systems and spent significant money, only to see the recommendations ignored. But these aren’t easy solutions to build. The reward is high, but so is the moat. This isn’t something you solve with software alone. I joined Hatch because I believed there was a class of meaningful industrial problems we couldn’t have solved at Infusion. Colleagues like Ryan Hunter and Aaron Le made the same move, and together we’ve seen first-hand how unlocking deep domain expertise changes what’s possible. That expertise lives here. It’s multi-disciplinary. It’s hard to bring together. But when it clicks, when AI meets engineering meets operations, the results speak for themselves. Where in your operations do you see these high-variable, high-impact decisions? That’s where the next big gains will come from. #AIinOperations #AIPC #IndustrialAI #ProcessControl #DigitalTransformation #EnergyEfficiency #MiningInnovation #RealImpact

Almost every AI value creation plan I see in PE is a cost takeout story. That is understandable -  cost is measurable, controllable, and shows up in EBITDA fast. But it misses the more consequential question for any hold-period thesis: What is AI doing to your target's top line? In industrial and services businesses, AI affects revenue in three distinct ways, and every portfolio company has some mix of all three. The composition determines whether the business is heading into a tailwind or a headwind, and most diligence processes are not equipped to tell the difference. AI-accelerated revenue. Categories where AI makes customers buy more, buy faster, or buy from a wider set of providers. Industrial distribution with AI-driven cross-sell and replenishment is a clean example. The technology expands share of wallet without a proportional increase in sales headcount. Field services companies using AI to shorten quote-to-cash cycles are compressing the sales funnel and winning business they previously could not staff. These are legitimate revenue tailwinds. AI-disintermediated revenue. Categories where AI removes the reason the customer was buying from a human intermediary in the first place. Specification-heavy distribution, basic engineering services, permitting support, certain categories of inspection and compliance work. The question is not whether these businesses can cut cost with AI- they can but whether their end customer will continue to pay the same price for a service that the customer can increasingly perform themselves with an AI tool. Margin compression often shows up here before volume decline does. By the time volume moves, the multiple has already re-rated. AI-neutral revenue. Categories where the buying decision, the delivery method, and the switching costs are structural and AI changes the back office but not the front office. Much of heavy industrial, regulated services, and physical field work sits here. The cost structure moves; the revenue does not. The uncomfortable truth: a target with strong Layer 1 AI cost takeout and an AI-disintermediated revenue base is a value trap. The margin expansion is real and the exit multiple compression is also real, and they cancel out, or worse. A diligence team that decomposes revenue into these three buckets before signing an LOI will catch this. A team that treats AI as a pure cost-side conversation will not.

𝗧𝗼𝗽 𝗔𝗜 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 Driving the Future of Manufacturing & Operations 🚀 and Revolutionizing Industries! Artificial Intelligence is no longer a futuristic concept. AI is actively transforming the industrial landscape and ecosystem. Delivering enhanced efficiency, cost savings, and quality improvements. For leaders and professionals in manufacturing, supply chain, and operations, understanding these core applications is crucial for staying competitive. Here are the game-changing industrial AI use cases you need to know: 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: Moving from reactive to proactive! AI analyzes sensor data from IIoT and edge devices to predict equipment failures before they happen, slashing downtime and maintenance costs. 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 & 𝐃𝐞𝐟𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: AI-powered computer vision spots minuscule defects at high speed, ensuring consistent product quality and significantly reducing waste. 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 & 𝐃𝐞𝐦𝐚𝐧𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Harnessing vast data, AI delivers accurate forecasts, optimizing inventory, logistics, and making supply chains more resilient. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 & 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: AI can monitor entire production lines, identifying inefficiencies and making real-time adjustments to boost throughput as well as reducing energy consumption. 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 & 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 (𝐂𝐨𝐛𝐨𝐭𝐬): AI empowers robots with the intelligence for complex tasks, enhancing precision, speed, and safety on the factory floor. 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬: Create virtual replicas of physical assets and processes, allowing for safe simulation, testing, and optimization without disrupting live operations. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐃𝐞𝐬𝐢𝐠𝐧: AI explores thousands of design options based on set constraints, accelerating product development and leading to innovative, high-performance designs. These applications are not just buzzwords. They are strategic investments yielding tangible ROI. Embracing AI is key to unlocking the next level of industrial performance and innovation! 💠 Which of these AI applications are you most excited about, or already implementing in your operations? Share your thoughts below! 💠 #AI #IndustrialAI #Manufacturing #Industry40 #DigitalTransformation #SupplyChain #PredictiveMaintenance #QualityControl #Robotics #Innovation #IIoT