AI in Manufacturing

# AI in Manufacturing Podcast ## Episode: Designing Autonomous AI Agents for Industrial Operations **Podcast Name:** AI in Manufacturing Podcast (Industry 40.tv) **Episode Title:** Designing Autonomous AI Agents for Industrial Operations **Guest:** Kence Anderson, CEO & Founder, AMESA **Host:** Kudzai Manditereza --- ## Episode Summary This episode explores how autonomous AI agents can transform industrial operations through a methodology called machine teaching. Kence Anderson, CEO and founder of AMESA, draws on eight years of experience applying autonomous systems to manufacturing and logistics to explain why more than 95% of what's called "industrial AI" today is really just data storage and connectivity — missing the actual intelligence layer that can perceive and act. Anderson breaks down his machine teaching methodology, which captures expert operator knowledge and structures it into teams of specialized AI agents that learn by practicing in simulation before deploying to the factory floor. The…

# AI in Manufacturing Podcast — Show Notes ## Episode: Scaling Industrial Intelligence with the I3X Common API **Podcast Name:** AI in Manufacturing Podcast (Industry40.tv) **Episode Title:** Scaling Industrial Intelligence with the I3X Common API **Guest Name:** Matthew Parris **Guest Title/Role:** Director of Quality Test Systems, GE Appliances; Leading Contributor to the I3X Specification **Host:** Kudzai Manditereza --- ## 1. Episode Summary This episode explores how the Industrial Information Interoperability Exchange (I3X) common API is poised to become the universal interface for accessing manufacturing data across software platforms. Matthew Paris, Director of Quality Test Systems at GE Appliances and a leading contributor to the I3X specification, explains why the manufacturing industry has lacked a standardized way to retrieve information from Level 3 and Level 4 software systems — and how I3X solves this by leveraging simple, proven IT technologies: HTTP and JSON. Paris draws a compelling analogy…

# AI in Manufacturing Podcast: Episode Show Notes ## Episode: Optimizing AI Inference for Agentic Operations in Manufacturing **Podcast Name:** AI in Manufacturing Podcast (Industry40.tv) **Episode Title:** Optimizing AI Inference for Agentic Operations in Manufacturing **Guest:** Kelvin Cooper, Co-Founder & CEO, Neurometric.ai **Host:** Kudzai Manditereza --- ## 1. Episode Summary This episode explores why manufacturing companies struggle to scale AI from pilot to production—and how inference orchestration and small language models (SLMs) offer a practical path forward. Kelvin Cooper, Co-Founder and CEO of Neurometric.ai, joins host Kudzai Manditereza to break down why routing all AI tasks through a single frontier model becomes a cost and reliability liability at scale. Cooper draws on his background in venture capital, private equity AI rollups at Pilot Wave Holdings, and AI policy research at the Milken Institute to argue that the future of industrial AI is not one model that knows everything, but a…

**Podcast Name:** AI in Manufacturing Podcast **Episode Title:** How to Build AI Solutions That Actually Work on the Factory Floor **Guest:** Renan De Villiers, Founder & CEO, OSS Ventures **Host:** Kudzai Manditereza --- ## 1. Episode Summary This episode explores why only 5% of factories currently operate like tech companies — and what it will take to reach 50% within a decade. Renan De Villiers, founder and CEO of OSS Ventures, a Paris- and Boston-based venture builder with 22 spun-out companies live in 3,800 factories worldwide, shares hard-won lessons from visiting over 900 manufacturing sites and deploying AI across 100+ factories in the past two years. Drawing on his background as a former McKinsey consultant, factory director, and tech startup founder, De Villiers explains why most manufacturing AI initiatives fail, how to industrialize the discovery process, and why designing the human experience of managing AI agents is the most underestimated challenge in scaling industrial AI. Listeners will…

## Episode: Building and Scaling Agentic AI Workflows in Manufacturing **Podcast Name:** AI in Manufacturing Podcast **Episode Title:** How to Build and Scale Agentic AI Workflows in Manufacturing **Guest:** Bernard Kraswald, Co-Founder & CTO at Ethon AI **Host:** Kudzai Manditereza --- ## Episode Summary This episode explores how manufacturers can build and scale agentic AI workflows to achieve operational excellence across factories. Bernard Kraswald, Co-Founder and CTO at Ethon AI, explains why traditional continuous improvement methods have reached their limits and how purpose-built industrial AI—grounded in process knowledge graphs and causal reasoning—unlocks the next wave of manufacturing optimization. Key insights include why deep data contextualization through knowledge graphs is essential for agentic AI (not just basic tag hierarchies), how causal AI differs from correlation-based analytics by making root cause findings actionable, and why a layered architecture of data infrastructure, specialized…

## Episode: The State of Industrial AI, Unified Namespace, and Knowledge Graphs After PROVE IT 2025 **Podcast Name:** AI in Manufacturing Podcast **Guest:** Walker Reynolds, President & Solutions Architect at 4.0 Solutions, Founder of the PROVE IT Conference **Host:** Kudzai Manditereza **Target Audience:** Manufacturing data leaders, IT/OT solution architects, and digital transformation professionals --- ## Episode Summary Walker Reynolds, President and Solutions Architect at 4.0 Solutions and founder of the PROVE IT conference, delivers an unfiltered assessment of where industrial AI actually stands in 2025. Drawing from conversations with over 1,000 attendees at this year's PROVE IT conference—70% of whom were end users working in manufacturing—Reynolds identifies three critical industry shifts: AI fatigue is setting in as vendors outpace market readiness, knowledge graphs have emerged as the essential technology for enabling agentic AI in manufacturing, and the gap between digitally mature and immature…

# AI in Manufacturing Podcast — Episode Show Notes ## Episode Details - **Podcast Name:** AI in Manufacturing Podcast (Industry40.tv) - **Episode Title:** Unlocking Productivity With Casual Models and Agentic AI in Manufacturing - **Host:** Kudzai Manditereza - **Guest:** Michael Carroll - **Guest Title/Role:** Strategic Advisor & Fellow COO Council at LNS Research; Chief Strategy Officer at Trek AI - **Target Audience:** Manufacturing data leaders, COOs, VP of Operations, IT/OT solution architects, and digital transformation professionals --- ## 1. EPISODE SUMMARY Agentic AI is not another digital tool to add to the manufacturing technology stack — it is a fundamentally different species of software that treats decisions, not transactions, as the atomic unit of work. In this episode, Michael Carroll, Strategic Advisor at LNS Research and Chief Strategy Officer at Trek AI, explains why US manufacturing productivity has been flat since 2010 despite massive investments in digital tools, and why agentic AI with…

Podcast Name: AI in Manufacturing Podcast (Industry40.tv) Episode Title: Context Engineering Techniques for Building Reliable Industrial AI Agents Guest: Zach Etier, VP of Architecture at Flow Software Host: Kudzai Manditereza Episode Summary This episode explores context engineering — the discipline of curating and managing the information supplied to AI agents — and why it is the key to building reliable industrial AI systems. Zach Etier, VP of Architecture at Flow Software, joins host Kudzai Manditereza to break down why simply pumping more data into an AI agent's context window actually degrades performance through dilution, hallucination, and lost instructions. Zach walks through three core context engineering techniques — persisting context, summarization/compaction, and isolation via sub-agents — and explains how each one maps to real manufacturing use cases like automated shift-handover reports. The conversation also covers the practical differences between skills, MCP servers, and sub-agents, and…

# AI in Manufacturing Podcast — Show Notes ## Episode: How to Reduce Waste and Improve Efficiency with AI-Powered Quality Control **Podcast Name:** AI in Manufacturing Podcast (Industry 4.0 TV) **Episode Title:** How to Reduce Waste and Improve Efficiency with AI-Powered Quality Control **Guest:** Willem Klein, CEO & Co-Founder, Zetamotion **Host:** Kudzai Manditereza **Target Audience:** Manufacturing data leaders, IT/OT solution architects, quality control professionals, and digital transformation leaders implementing AI in industrial operations --- ## 1. Episode Summary This episode explores how AI-powered quality control can reduce waste and improve efficiency in manufacturing, featuring Willem Klein, CEO and co-founder of Zetamotion. Willem shares why over 90% of industrial AI pilots fail and explains that the real competitive advantage lies not in building bigger AI models, but in designing better end-to-end systems that integrate seamlessly into existing production environments. He introduces Zelia,…

Episode Title:** Practical Guidance for Implementing Industrial AI Agents in Manufacturing Guest:** James Zheng, Co-Founder & Chief Product Officer, Optimate AI Host:** Kudzai Manditereza --- ## 1. Episode Summary This episode explores how agentic AI is creating a new category of digital skilled workers for manufacturing, addressing the industry's deepening productivity plateau and skilled labor crisis. James Zheng, Co-Founder and Chief Product Officer of Optimate AI, draws on over a decade of experience building and deploying manufacturing software — from SAP's cloud ERP to PTC's ThingWorx IoT platform — to explain why traditional digital transformation investments have failed to move the productivity needle. Zheng introduces the concept of a "decision intelligence and execution layer" that sits on top of existing systems of record (MES, ERP, CMMS, SCADA) to orchestrate AI agents that augment engineers, technicians, and frontline leaders. The conversation covers practical adoption patterns, the critical…

1. EPISODE SUMMARY This episode explores why most manufacturing AI initiatives fail and what companies must do to build a foundation for AI-native industrial intelligence. Craig Scott, Founder and CEO of Fuuz, an industrial intelligence platform, shares insights from nearly a decade of bridging the gap between shop floor data and enterprise systems. The conversation reveals why the missing "shim" between operational technology and enterprise systems is the root cause of unreliable data in manufacturing, and why model-driven approaches are essential for scaling AI across industrial operations. Craig explains how organizations can achieve a single source of truth by implementing a persistent contextualization layer that governs data before AI ever touches it. Whether you're struggling with fragmented point solutions, evaluating industrial data platforms, or preparing your data infrastructure for AI, this episode provides a practical framework for building scalable industrial intelligence. 2. KEY QUESTIONS…

Traditional MES platforms were built for a manufacturing world that no longer exists. They assume stable product lines. They assume you have time for lengthy implementations, tolerance for complexity, and operators who can navigate digital forms while running production. But here's the challenge. Today's manufacturing reality is different: ⇨ Markets demand the flexibility to shift from 1.5-liter bottles to 1-liter bottles overnight ⇨ Low volume, high mix production is now the norm ⇨ Tribal knowledge is retiring faster than it's being captured ⇨ Workers stay 2-3 years, not 20, making traditional training models obsolete The cost of this disconnect? ❌ Frontline workforce unable to contribute operational intelligence at scale ❌ ROI delayed by complexity, not capability ❌ Two-year deployment cycles for basic systems ❌ Digital initiatives stuck in pilot purgatory That's why leading manufacturers are rethinking execution from the ground up, shifting from monolithic systems to AI-native, human-centric platforms…

Agentic AI Framework for Manufacturing Operations AI in Manufacturing Podcast Show Notes Episode Guest: Gilad Langer, Head of Digital Transformation Practice at Tulip Interfaces Host: Kudzai Manditereza Publication Date: [Insert Date] Episode Summary Manufacturing systems are complex adaptive systems that require a fundamentally different approach to AI implementation than traditional monolithic architectures. In this episode, Gilad Langer draws on 30 years of manufacturing experience—including PhD research that laid the groundwork for Industry 4.0—to introduce a composable agentic framework specifically designed for frontline operations. He explains why adaptability has become a competitive necessity in today's disrupted markets and how multi-agent systems can transform innate factory equipment into intelligent, communicating entities. The conversation covers practical implementation strategies, the artifact model for structuring manufacturing data, and why cultural change remains the biggest obstacle to…

Context isn't static. It's a living layer of knowledge built through problem-solving, conversation, and understanding the complex relationships on the factory floor. This simple truth is often overlooked in industrial data strategies. We’ve been conditioned to believe that context can be predefined; baked into standards, taxonomies, and hierarchies. But in real-world manufacturing, things change, people think differently, and use cases evolve. So how can we build this dynamic layer of understanding for industrial AI? In our latest AI in Manufacturing episode, I spoke with Bob van de Kuilen, Director at Thred, about a more human-centric approach to industrial data contextualisation using Knowledge Graphs. Thred is a tool that plugs into Ignition Platform, enabling users to visualize their factory assets in a knowledge graph, link related data points, embed domain expertise, and deliver structured, contextualized data to AI and analytics tools. We discuss: ✅ Why traditional approaches to data context often…

SA-95 is a standard that’s often misunderstood, but incredibly powerful. While many think ISA-95 is rigid or overly complex, it actually enables flexibility by: ⇨ 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐚 𝐬𝐡𝐚𝐫𝐞𝐝 𝐯𝐨𝐜𝐚𝐛𝐮𝐥𝐚𝐫𝐲 for manufacturing concepts, creating a true ontology for your data. ⇨ 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐩𝐥𝐚𝐜𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 for every type of data, so you can start small and add new use cases later without rebuilding everything. ⇨ 𝐏𝐫𝐨𝐯𝐢𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 "𝐰𝐡𝐲" 𝐛𝐞𝐡𝐢𝐧𝐝 𝐞𝐯𝐞𝐧𝐭𝐬, not just the "what," giving crucial context to your analytics and AI models. But how do you move from theory to a practical, modern implementation? In our latest AI in Manufacturing podcast episode, we explore exactly that with ISA-95 expert Jeroen Janssen, who is an MES/MOM Consultant at Rhize Manufacturing Data Hub. In the episode, you’ll learn: ✅ How to overcome a data culture that creates so many silos. ✅ The "use case stacking" method for a phased, value-driven implementation. ✅ What a native ISA-95 data hub looks like and how a graph database can bring it to life. ✅ Why this…

Is the Timebase free historian getting an AI-Native DataOps component with Knowledge Graphs capability? You’ll hear it here first. In the latest episode of the AI in Manufacturing podcast, I sit down with Jeff Knepper, President at Flow Software Inc., to discuss the intersection of Information Management and AI in modern manufacturing, plus the exciting announcement of Timebase Atlas launch. Here’s some of what we cover in this episode: ✅ Why manufacturers struggle to make use of their data ✅ Building reliable pipelines for AI-driven use cases ✅ AI Agents in Manufacturing – Where they fit and what they need ✅ Unified Analytics Framework vs. Unified Namespace ✅ Historization Strategies – Best practices from edge to cloud ✅ Timebase Atlas Launch Announcement: Data Modeling, Pipelines, Knowledge Graphs, and AI interfaces ✅ MCP and Flow AI Gateway: Beyond APIs to Context-Aware Agent Interfaces

Most data-quality initiatives focus on things like freshness or schema. That works for IT data, but not for sensor data. Sensor data is different. It reflects physics. To trust it, you need contextual, physics-aware checks. That means spotting: → Impossible jumps → Flatlines (long quiet periods) → Oscillations → Broken causal patterns (e.g., valve opens → flow should increase) It’s no surprise that poor data quality is one of the biggest reasons manufacturers struggle to scale AI initiatives. This isn’t just data science, it’s operations science. Think of data quality as infrastructure: a trust layer between your OT data sources and your AI tools. Making that real requires four building blocks: 1. 𝐒𝐜𝐨𝐫𝐢𝐧𝐠 – Physics-aware anomaly rules, baselines 2. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 – Continuous validation at the right cadence (real-time or daily) 3. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 – Auto-fix what you can; escalate what you can’t 4. 𝐔𝐧𝐢𝐟𝐨𝐫𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐒𝐋𝐀𝐬 – Define “good enough” and enforce it before data is consumed Why it matters: ✅ Data…

What really makes data and AI innovation teams succeed in manufacturing? In this episode of the AI in Manufacturing Podcast, I speak with Van Tucker, VP of Harbor Lockers by Luxer One, a company that develops and manufactures smart public lockers. We discuss the challenges and strategies for building effective innovation teams in manufacturing. Here are some of the insights that Van shared: 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 Innovation thrives when people, from the boardroom to the factory floor, believe in the mission. Core values must be lived daily, not just written on posters. 𝐀𝐠𝐢𝐥𝐢𝐭𝐲 𝐨𝐯𝐞𝐫 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧 Instead of waiting months for a polished rollout, start simple. Test small ideas quickly, gather feedback, and iterate. Even in hardware manufacturing, lightweight R&D “sandboxes” allow experimentation without disrupting core production. 𝐌𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞 𝐚𝐧𝐝 𝐛𝐮𝐫𝐧𝐨𝐮𝐭 Burnout shows up in declining quality and disengagement. The best leaders don’t wait, they stay close to their teams, recognize early warning signs, and…

Most industrial processes still run on the same foundation: - Hard-coded logic in PLCs that follows predefined rules. - The intuition of process and plant engineers, built from years of experience. This combination has powered industry for decades, but it has limits. When the challenge involves many interacting variables, unknown relationships, and non-linear effects, traditional control starts to strain. Why? Because fixed rules can’t adapt fast enough to changing conditions, and even the best human intuition can only process so much complexity at once. Instead of relying on fixed instructions, RL agents learn directly from real-time feedback. They can: ✅ Adapt continuously to new conditions. ✅ Handle high-dimensional problems with countless variables. ✅ Uncover novel, more efficient strategies that humans might overlook. The result? An optimization layer that works alongside your existing control system, making it smarter, more adaptive, and capable of delivering gains where complexity used to be a…

Can AI agents really make decisions in high-stakes industrial environments? Generative AI agents, on their own, do not have a robust understanding of cause-and-effect for real-world decision-making. However, when combined with Deep Reinforcement Learning, AI agents gain the ability to reason, learn from interaction, and make decisions that solve operational problems in complex, real-world environments, like the plant floor. Case in point. Bryan DeBois and his team at RoviSys developed an Autonomous AI agent to manage a notoriously difficult glass bottle production process, where small disruptions like temperature fluctuations can quickly push the process out of specification. Here’s how they approached it: ✅ 𝐒𝐭𝐞𝐩 1 - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠 They captured the knowledge and decision-making strategies of expert human operators and used this to train the AI agent, essentially teaching it how to respond to different operating conditions. ✅ 𝐒𝐭𝐞𝐩 2 - 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐌𝐨𝐝𝐞 Initially, the agent didn’t control the process…

The industrial data stack was never built for enterprise-wide intelligence. It was built in silos, optimized for local decisions. As a result, it is not designed to support unified, contextualized, and scalable data management across an organization. And that’s why Industrial Data Platforms are essential for scaling digital transformation. To help organizations understand what makes such a platform effective, David Ariens and The IT/OT Insider team created the Industrial Data Platform Capability Map, outlining the seven key capabilities every platform should have: 1. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 – a secure and scalable connectivity layer to integrate different data sources into the Industrial Data Platform. 2. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – delivering data enriched with the right context, so users don’t have to gather and piece together information from multiple sources manually. 3. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 – Detecting and fixing data issues in your pipeline, from sensor to final report. 4. 𝐃𝐚𝐭𝐚 𝐁𝐫𝐨𝐤𝐞𝐫 𝐚𝐧𝐝 𝐒𝐭𝐨𝐫𝐞 – The ability to…

Instead of sending data to the cloud for processing, Edge AI analyzes data right where it’s generated, on the machine, in the plant, in real time. It’s the difference between reacting later and responding now. What Happens When You Keep Intelligence at the Source? 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 A conveyor motor vibrates abnormally. Edge AI detects the anomaly instantly and slows the line before damage occurs. 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 Time-series models forecast when a press will wear out, so teams fix it during scheduled downtime, not after it fails. 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐝𝐠𝐞 Cameras inspect every product. Edge AI flags visual defects without ever uploading a frame to the cloud. In the latest episode of the AI in Manufacturing podcast, I sat down with Rainer Maidel, Business Development Manager at BE.services GmbH, the creators of Coligo Edge AIoT Software. We had an in-depth discussion about the application of Edge AI in the digitalization of industrial processes.

The first foundation model purpose-built for refining and petrochemicals? Here's the thing. The oil, gas, and petrochemical industry is under pressure like never before. ⇨ Demand is set to double in 15 years ⇨ Facilities are shutting down ⇨ Energy transition is colliding with operational cost realities At the same time, companies are being told AI will solve it all. But here’s the truth. Most AI was built for the internet, not industrial plants. ❌ It can’t explain its decisions ❌ It hallucinates ❌ It’s fragile with messy, real-world data ❌ It struggles with incomplete time series and unstructured reports Now apply that to a refinery running 24/7, filled with volatile compounds and extreme conditions. And you start to see the problem. AI that can’t be trusted is worse than no AI at all. That’s why Callum Adamson and his team built Orbital. The first foundation model designed specifically for refining and petrochemicals. Instead of trying to stretch general-purpose AI into high-consequence environments,…

Part traceability in manufacturing has long relied on traditional barcodes that fail where it matters most: under heat, blasting, and coating, e.t.c. As a result, manufacturers normally place barcodes after key part transformations. That means, for 70%+ of the production process, you're flying blind. You're guessing which parts went through which treatments. And when something fails? You're looking at massive recalls, supplier penalties, and lost time. What if we could code physical parts in a way that never fades? That’s exactly what Serra Tuzcuoglu and her Co-Founder invented. CDOT AI Code, a frequency-based, AI-readable identifier that can survive the harshest industrial conditions. Unlike traditional visual patterns, it uses signal recognition instead of contrast, enabling it to be read even when surfaces are scratched, painted, or distorted. A global appliance manufacturer gave them a challenge: “If your code can survive enamelling and high heat, we’ll use it.” It did. That was the beginning. CDOT AI…

Learn how Jonathan and his team at Albemarle Corp went from pilots to $150M in annual improvements through a business-first, scalable AI strategy. In the latest episode of the AI in Manufacturing podcast, I spoke with Jonathan Alexander, Global Manufacturing AI and Advanced Analytics Manager at Albemarle Corporation, about building, scaling and sustaining AI-driven Transformation in Manufacturing. Here’s the outline of our conversation: ⇨ Key Data Challenges in Implementing AI at Scale ⇨ Data Contextualization for Analytics and Decision Making ⇨ Data Architecture & Interoperability ⇨ Standardization & Scaling of AI Applications ⇨ Driving Sustained Action from AI Insights ⇨ Sustaining AI Adoption & Value Creation on the Plant-Floor ⇨ Change Management & Culture

Industrial teams still rely on fragmented and manual processes to match complex product specs with use-case-specific needs. Take this example: You're selling a vision sensor to a factory. To get it right, you need to know: ⇨ What’s the size and speed of the conveyor line? ⇨ Is the plant located in Munich or Arizona? ⇨ Will this sensor withstand that temperature range? ⇨ What PLC is the customer using — Siemens or Rockwell? ⇨ Will the sensor integrate without conflict? ⇨ Are there newer models in the portfolio that fit better? ⇨ Can it be installed without disrupting production? Now imagine trying to answer all of that... ⇨ Using PDFs. ⇨Email chains. ⇨ Gut instinct. ⇨ And hoping Bob from Engineering isn’t on vacation. With an AI Agents trained on your connected industrial knowledge: ✅ All technical documentation, manuals, spec sheets, CAD drawings, becomes queryable ✅ Reps and engineers can ask natural-language questions and get verified answers ✅ Compliance, compatibility, and environmental fit can be…

In theory, AI should learn, adapt, and improve continuously. But in reality, most deployments are static and disconnected from the evolving complexity of shop floor operations. Most businesses lack tools to close the loop between: ⇨ Data collection ⇨ AI training ⇨ Deployment ⇨ Continuous retraining ⇨ Business impact validation And they struggle to connect domain experts with data scientists. To learn more about building and scaling closed-loop AI for industrial operations I recently sat down with Dr. Nikita Golovko who is a Software Architect for Industrial AI at Siemens.

Many small and mid-sized manufacturers want to explore AI to improve efficiency, reduce waste, or make their processes smarter. However, this process requires OT and IT knowledge not present in many industrial companies, mainly SMEs. Ander Garcia Gangoiti and his team built a micro-service edge architecture based on MQTT, TimescaleDB, Node-Red and Grafana stack to ease the integration of soft AI models into industrial system. The architecture has been successfully validated controlling the vacuum generation process of an industrial machine. Soft AI models applied to real-time data of the machine analyze the vacuum value to decide when the most suitable time is: ⇨ to start the second pump of the machine, ⇨ to finish the process, and ⇨ to stop the process due to the detection of humidity. Ander is the Director of Data Intelligence for Industry at Vicomtech and I recently sat down with him on the AI in Manufacturing podcast.

Imagine a control system that learns, optimizes in real-time, and integrates seamlessly with both field assets and cloud-native AI platforms. This is the next chapter of industrial process automation. Already implemented at the largest Oil refinery in the world, Software-defined control systems break the traditional link between hardware and logic. This separation allows for dynamic control, centralized intelligence, and flexible deployment across complex industrial environments. When integrated with time-series foundation models, these systems harness AI for intelligent loop control, advanced process optimization, and even reinforcement learning, driving unprecedented levels of performance in control environments. In the latest episode of the AI in Manufacturing podcast, I sat down with Huize Zhang to explore this transformation. Huize is the Vice President at SUPCON, China’s leading DCS provider, and the founder of FREEZONEX, an open-source IIoT platform. Here’s the outline of our conversation: -The…

In manufacturing, time-series data is everywhere, but most plants are still relying on static dashboards, lagging insights, and manual root-cause analysis. The result? - Downtime that’s explained, not prevented - Insights that arrive, after the line slows down - Human effort wasted on repeat investigations AI agents transform the way manufacturers harness time-series data. They process live sensor feeds while simultaneously referencing historical records, enabling instant anomaly detection and context-aware decisions. They can correlate vast time-series data with external factors to uncover insights missed by rigid statistical models. They can trigger actions like maintenance tickets or production adjustments directly from analytics, bypassing manual interpretation steps. They connect the dots across thousands of data streams in real time, automatically identifying root causes and recommending actions on the fly. In the latest episode of the AI in Manufacturing podcast, I sat down with Jeff Tao to learn more…

Learn how Joao and and team are using Knowledge Graphs and IIoT to power Industrial AI and Digital Twin use cases at Scania. Here’s the outline of our conversation: Core Challenges in Managing Industrial Data for Data‑Driven Manufacturing The Role of Ontologies and Knowledge Graphs in Advancing Industrial Data Interoperability and Analytics IIoT Data Integration and Standardization Approaches Semantic‑Modeling Best Practices for Scaling Value Creation Using Knowledge Graphs as Infrastructure for Digital Twins and Industrial AI Industrial AI Use Cases Powered by Knowledge Graphs The Real Business Value of Digital Twins in Manufacturing Building the Next-Gen Digital Twins with AI, LLMs, and Knowledge Graphs AI Agents, and MCP for Distributed Intelligence on Digital Twins Multi-Agent AI Systems for the Future of Manufacturing Digitalization

As manufacturing demands increase, integrating AI-powered visual systems into quality inspection processes becomes increasingly beneficial. While traditional inspection methods have been the cornerstone of quality control in manufacturing, they come with limitations such as subjectivity, fatigue, and scalability challenges. AI-powered visual inspection systems address these issues. Leveraging advanced algorithms and machine‑learning models, they analyze images with high accuracy, identifying defects that may be invisible to the human eye. This not only enhances the reliability of quality assessments but also increases operational efficiency, allowing manufacturers to streamline their processes and reduce costs. The capability to detect anomalies in real-time empowers companies to address issues before they escalate, ensuring that only the highest-quality components progress through production. To find out more about the application of Visual AI Inspection in manufacturing, I recently sat down with Priyansha…

Modern manufacturing environments generate a staggering amount of data from machines, processes, quality checks, logistics, and inventory. And yet, most of it goes unseen, unused, and unanalyzed. Why? Because the data is too vast, too fast, and too fragmented for any human to handle in real-time. Even the best engineers can’t monitor thousands of variables 24/7. And failing to harness this data has real consequences. Critical warning signs of equipment problems or process inefficiencies can be missed, leading to unplanned downtime and quality issues. The biggest challenge AI Agents solve in industrial enterprises is transforming this overwhelming amount of complex data into actionable intelligence. However, AI Agents are only powerful for manufacturing data analytics when paired with the right context. That means feeding them, sensor data, maintenance logs, ERP & MES records, operator notes, engineering drawings, and SOP documents e.t.c. And quickly surfacing the most relevant information to power rapid…

Every minute a machine is offline costs money. That’s why Mean Time to Repair (MTTR) is one of the most vital metrics in manufacturing. It tells you how fast your team can identify an issue, find the solution, and get the line moving again. Unfortunately, in many facilities, this process is slow and cumbersome: when a technician sees an error code, they often have to sift through hundreds of pages of documentation while the clock is ticking. A long MTTR doesn’t just mean downtime; it means: - Lost production - Missed delivery deadlines - Heightened stress on frontline teams - Frustration for leadership and customers By using Generative AI to access your entire library of manuals, maintenance logs, and SOPs, maintenance teams can quickly find the answers they need and take swift action to minimize downtime. To learn more about Reducing Machine Downtime with AI-Powered Knowledge Management I had a chat with Jose Dos Santos, Co-Founder and CEO of Industrial AI

For decades, manufacturers have relied on traditional analytics—correlations, trendlines, dashboards—to make operational decisions. But there's a limit: Correlation ≠ Causation Just because two variables move together doesn’t mean one causes the other. This blind spot can lead to poor decisions and surface-level fixes that don’t solve the real issue. For example, a machine’s temperature spikes often coincide with defects. Traditional analytics might alert you when it happens—but not why. Is it the temperature? A faulty sensor? Operator error? Causal Inference flips the script. Instead of just observing data patterns, it asks: “What actually caused this outcome?” I recently sat down with Daniele Gamba, CEO of AISent Srl to learn more about building industrial intelligence solutions with Caussal AI.

Manufacturing leaders are familiar with physical waste; scrap, rework, and inefficiencies in production. But digital waste is the hidden inefficiency that’s just as costly. It includes: 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚: Factories generate massive amounts of data, but much of it is never analyzed or leveraged for decision-making. 𝐈𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: Engineers waste time manually entering, cleaning, or searching for information that should be automated. 𝐒𝐢𝐥𝐨𝐞𝐝 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Key insights are trapped in different departments or legacy systems, preventing AI-driven optimization. Digital waste silently drains resources, increasing operational costs while blocking AI from delivering its full potential. Once manufacturers recognize digital waste, the next step is identifying where AI can generate the biggest returns. To learn more about finding opportunities for the application of AI in manufacturing, I recently sat down with Patrick Byrne, Co-Founder and CEO of Annora AI.

Manufacturers are constantly battling two critical challenges: Inefficiencies in Equipment Usage: Downtime, slow cycle times, and unidentified bottlenecks reduce Overall Equipment Effectiveness (OEE), leading to wasted resources and missed production targets. Safety Risks: Ensuring worker safety while maintaining productivity is difficult, especially in environments with heavy machinery and fast-moving processes. Despite best efforts, traditional methods struggle to keep up with the complexity and speed of modern manufacturing. By using computer vision and deep learning, Video AI Agents bring continuous, detection and response of issues—far beyond what traditional methods alone can achieve. I recently sat down with Karim Saleh, Co-founder and CEO at Cerrion to learn more about how to Maximize OEE and production Line Safety with Video AI Agents

AI’s success in manufacturing depends on the ability to seamlessly integrate data from machines and systems across the factory floor and supply chain. Without strong connectivity, AI remains underutilized, limited by data silos, and inconsistent integration. Connectivity isn’t just about linking devices; it’s about creating a unified data environment where AI can operate at its full potential—powering everything from predictive maintenance to automated quality control and beyond. To learn more about IT/OT connectivity for enabling AI use cases in manufacturing I had a conversation with Bernd Hafenrichter who is the CTO of soffico GmbH.

Frontline workers are the backbone of manufacturing, but they’re often held back by manual data entry, process inefficiencies, and knowledge gaps. AI-powered Industrial Copilots offer a solution that elevates their capabilities: 𝐍𝐨 𝐌𝐨𝐫𝐞 𝐌𝐚𝐧𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐄𝐧𝐭𝐫𝐲 AI Copilots automate data capture and seamlessly integrate with existing systems—eliminating wasted time and inaccuracies. 𝐒𝐦𝐚𝐫𝐭𝐞𝐫, 𝐅𝐚𝐬𝐭𝐞𝐫 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 AI surfaces real-time insights, helping teams reduce downtime, optimize production, and make data-driven decisions on the fly. 𝐂𝐥𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐆𝐚𝐩 AI-driven step-by-step guides provide instant troubleshooting and best practices, ensuring even new employees perform like seasoned experts. 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 As operations grow, AI Copilots adapt to new processes, machinery, and industries, ensuring a future-proofed approach to efficiency and innovation. To learn more about the application of AI Copilots for enhancing Frontline Operations in Manufacturing, I had a chat with Mason Glidden Chief Product and…

Many factories today grapple with recurring production issues and inefficiencies; whether it’s inconsistent quality, unpredictable downtime, or process bottlenecks. The cost of inefficiencies keeps mounting, and while human intuition and manual checks have been valuable tools, they’re no longer enough to drive significant breakthroughs. AI offers an opportunity to uncover hidden patterns that human teams might miss. For instance: - By analyzing machine sensor data, AI can trace yield drops to subtle temperature fluctuations. - AI can identify bad material batches from suppliers or reveal operational bottlenecks. - Instead of vague reports, AI delivers precise, actionable insights, helping teams shift from guesswork to targeted, data-driven solutions. To learn more about how Manufacturers can achieve operational excellence through data-driven manufacturing optimisation with AI, I had a conversation with Zhitao(Steven) Gao who is the CEO and Co-Founder of eXlens.ai.

While the promise of AI is immense, many manufacturers find themselves stuck in pilot projects, unable to unlock its full potential. The key lies in addressing foundational challenges and adopting a clear, phased strategy to transform operations. Fundamentally, AI offers manufacturers a pathway to achieving operational excellence by moving through the four stages of analytics maturity: 1️⃣ Descriptive Analytics – Understanding what happened. 2️⃣ Diagnostic Analytics – Pinpointing root causes. 3️⃣ Predictive Analytics – Forecasting potential equipment failures or quality issues. 4️⃣ Prescriptive Analytics – Recommending the best actions to address challenges. Despite its promise, many manufacturers struggle with significant obstacles, which include data fragmentation. I recently had a sit down with Andrew Scheuermann the CEO and Co-Founder of Arch Systems to discuss why building a comprehensive Digital Twin is the key to overcoming these barriers and how manufacturers can use AI to enhance manufacturing…

In our latest episode of the AI in Manufacturing Podcast, I sat down with Zeeshan Zia, co-founder and CEO of Retrocausal, to dive deep into how AI co-pilots are transforming the manufacturing sector. Here are three key takeaways: 1️⃣ Labor Challenges Meet Smart Solutions Manufacturers face critical labor shortages, resulting in significant costs. Zeeshan shared how AI-powered Assembly Co-Pilots are slashing error rates and scrap costs by up to 90% while empowering workers with real-time guidance. 2️⃣ Merging Lean Principles with AI Traditional lean manufacturing focuses on quality, productivity, and safety. RetroCausal’s tools like Kaizen Co-Pilot and Ergo Co-Pilot seamlessly integrate lean methodologies with advanced AI, accelerating time studies and ergonomic assessments in hours instead of weeks. 3️⃣ Scalability Across Diverse Workflows From discrete manufacturing to medical devices, AI co-pilots are not just for single processes—they scale efficiently across multiple sites, even in highly regulated…

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Today's manufacturing industry faces significant challenges in managing its data environment. Vast amounts of unorganized data collected from various sources often become "data swamps," making it difficult to extract meaningful insights and generate value. This overwhelming complexity hinders decision-making and slows down innovation. Additionally, the analytics tools currently available are often too complex and static for domain experts to use effectively, leaving them without the critical insights needed to improve processes, optimize production, and make informed decisions. AI assistants offer a promising solution by bridging the gap between complex data sets and user-friendly interfaces. They transform unstructured data into actionable insights accessible to everyone in the organization. To learn more about the application of AI assistants for advanced manufacturing data analytics, I sat down with Stefan Suwelack, the CEO and Co-Founder of Renumics.

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In this episode, we explore how artificial intelligence is transforming manufacturing from the ground up. We dive into cutting-edge applications and discuss the benefits and challenges AI introduces to the industry. Here’s a sneak peek at what we cover: 1. Predictive Maintenance for Machinery AI helps manufacturers predict equipment failures before they happen, reducing downtime and saving costs. With predictive maintenance, companies can transition from reactive to proactive maintenance, leading to longer machine life and fewer unexpected breakdowns. 2. Quality Control and Defect Detection AI-powered visual inspections now identify defects faster and more accurately than human inspectors, ensuring product consistency and quality. We discuss how AI-driven quality control is reducing waste and improving overall customer satisfaction. 3. Supply Chain Optimization AI tools are optimizing supply chains by predicting demand and adjusting inventory accordingly. In the episode, we break down how smarter supply…

While large language models hold immense potential, there's a significant gap between what these tools offer out of the box and what the manufacturing industry needs. Manufacturing presents unique challenges that generic AI solutions often can't effectively address. However, by customizing Generative AI systems to meet industry-specific requirements, this gap can be effectively bridged: - Tailoring AI to understand specialized language and scenarios enhances its relevance and effectiveness. - Integrating additional data sources, such as knowledge graphs, enriches the AI's understanding of relationships and processes unique to manufacturing. - Implementing safety checks and operational boundaries ensures that AI recommendations are viable, safe, and compliant with industry standards. When these measures are in place, Generative AI becomes a powerful tool applicable to a wide range of use cases. Tune in to the full episode with Vlad Larichev, the Industrial AI Lead at Accenture Industry X to learn more about…

In this episode, I sat down with Michael Kuehne-Schlinkert, CEO of Katulu to discuss how Federated Machine Learning is transforming industrial AI. Here are some key takeaways: Federated Learning Enables Cross-Factory Collaboration Federated learning allows multiple factories to improve AI models without sharing sensitive data. By exchanging learnings, factories can build more robust models while maintaining data privacy and compliance. Collaboration on Model Training Without Compromising Privacy One of the biggest challenges in industrial AI is accessing the right data without compromising privacy. Federated learning addresses this by keeping sensitive data local, allowing companies to enhance their AI models collectively without exposing each other’s proprietary or sensitive information. Cost-Effective Scaling of AI Models Through Reuse Scaling AI across multiple factories typically involves high costs and complexity. Federated learning significantly reduces development, integration, and operation costs by…

In the latest episode of the AI in Manufacturing podcast on Industry 4.0 TV, host Kudzai Manditereza sits down with Ashan Posohi, CEO and co-founder of Third Wave Automation, to explore how AI-powered robots are transforming material handling. The focus is on autonomous forklifts and their impact on productivity, safety, and the future of manufacturing. Arshan Poursohi brings a rich background in robotics and research, having worked with Sun Microsystems, Google Research, and Toyota Research. His experiences exposed him to global labor challenges, such as aging workforces and a shortage of young people entering physically demanding jobs. Recognizing the urgent need to address these issues, Arshan founded Third Wave Automation to apply modern AI and robotics solutions to material handling in warehouses and manufacturing environments. The Business Benefits of AI-Powered Robots The discussion highlights how AI and robotics are revolutionizing the manufacturing industry by automating "dull, dirty, and dangerous"…

In this episode, I sat down with Yousef Mohassab, CEO of Facilis.ai, to explore how Agentic AI is transforming the manufacturing industry. If you're looking for practical insights on scaling AI and boosting operational efficiency, this is the episode you can't miss! Here are the key takeaways: The Shift from Centralized to Agentic AI Manufacturers can no longer afford to rely on centralized data science teams that create bottlenecks. Agentic AI empowers subject matter experts (SMEs) to directly access and analyze data, significantly cutting down response times from weeks to minutes. A Human-Level Automation Approach Agentic AI mirrors human problem-solving by breaking down complex challenges into smaller tasks. This approach enables real-time, adaptive solutions that evolve with the operational landscape, especially when processes change or equipment gets upgraded. Beyond Traditional Machine Learning Unlike static models that rely on historical data, self-adaptive systems continuously learn in real-time.…