Building and Scaling AI-Driven Transformation in Manufacturing: Jonathan Alexander - Global Manufacturing AI and Advanced Analytics Manager, Albemarle Corporation

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 Code Code is now deployed globally, from Renault crankshafts to Ford EV battery trays, military tanks to printing cylinders.   In the latest episode of the AI in Manufacturing podcast, I sat down with Serra Tuzcuoglu, the CEO and Co-Founder of COSMODOT, to discuss:   ✅ How CDOT AI Code works ✅ Solution Architecture & Integration into the factory network ✅ Readiness for AI-based quality analytics and creation of digital twins. ✅ Real-world examples and Case Studies

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 checked in seconds ✅ Human experts stay in the loop, but no longer stuck in the weeds I recently sat down with Fay Goldstein Co-Founder and CEO of Folio to discuss the application of AI Agents for Industrial Sales and Application Engineers. ABOUT FOLIO: Folio’s AI platform empowers industrial sales and application engineers by turning technical specs, configuration data, and application info into instant answers, recommendations, and agentic workflows, speeding work, cutting errors, and boosting revenue for industrial manufacturers and distributors. Learn more at www.folio.build ABOUT FAY: Fay Goldstein is the Co-Founder and CEO of Folio, an AI-powered platform that transforms how manufacturers and distributors sell and support complex and technical industrial product portfolios. Before founding Folio, she spent her summers managing direct and online sales at local automotive AC condenser and compressor shop, led strategic GTM and communications at an automotive telematics data company, and worked at an early-stage venture capital firm, where she supported dozens of early-stage startups on their initial GTM and communication strategies. Fay graduated magna cum laude from Florida International University and holds an MBA from Reichman University.   CONNECT WITH FAY 🌐 Website: https://www.folio.build/ 💼 LinkedIn: https://www.linkedin.com/in/faygoldstein/

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 Control Platform of The Future -Open Standards and Platforms -AI-Driven Optimization in Process Industries -Time-Series Pre-Trained Transformers -Reinforcement Learning in Process Industries -UNS Integration with AI Agents

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 about the application of AI Agents for Advanced Time Series Data Analytics. Jeff is the CEO and Founder of TDengine, the developers of TDengine an IIoT time-series database, TDgpt time-series AI Agent, and TDtsfm, a Time-Series Foundation Model.

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 Bagaria who is the Founder and CEO of Loopr AI. 

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 AI-driven decision-making. This is where Vector Storage and Search comes into play. To learn more about Vector Databases and Data Structure for Industrial AI Agents I had a chat with Humza Akhtar, PhD who is the Senior Industry Principal for Manufacturing and Automotive at MongoDB.