Most data-quality initiatives focus on things like freshness or schema. That works for IT data, but not for sensor data.

What really makes data and AI innovation teams succeed in manufacturing?

Most industrial processes still run on the same foundation:

Can AI agents really make decisions in high-stakes industrial environments?

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.

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.

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.

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