AI in Manufacturing

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 ingest, store, and manage contextualized data at scale, enabling efficient data subscription and large-scale querying. 

5. 𝐄𝐝𝐠𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 – The capability to perform analytics and machine learning within the data platform, or at the edge, close to where the data is generated.

6. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 – The capability to deliver high-quality, contextualized data to users through intuitive and accessible interfaces for fast, informed decision-making.

7. 𝐃𝐚𝐭𝐚 𝐒𝐡𝐚𝐫𝐢𝐧𝐠 – The capability to openly expose platform data to external users and applications through standard interfaces and integrations.

In the latest episode of the AI in Manufacturing podcast, I sat down with David, Founder of IT/OT Insider, to dive deeper into these capabilities and how organizations can implement them.

We also discussed the IT/OT Academy, an online training program designed to help IT and OT professionals build a shared vocabulary, framework, and collaboration strategy to move digital initiatives beyond pilot projects and into full-scale plant deployment.

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?

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.

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

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.