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.

[0:00] - Introduction — Craig Scott's background from hands-on manufacturing at age 15 to founding Fuuz, and why the company's purple branding represents the merger of "red" (OT) and "blue" (IT) data.

[6:56] - What's Fundamentally Broken — Discussion of how critical manufacturing knowledge is leaving the business as the workforce ages, and why data-driven approaches are essential to capture and retain institutional knowledge.

[8:09] - The Missing Shim Problem — Craig explains the gap between real-time shop floor data (SCADA/historians) and enterprise systems (ERP/PLM), and why neither system alone provides a single source of truth.

[16:20] - MCP and I3x Integration — How Fuuz is implementing Model Context Protocol and aligning with the I3x initiative for standardized GraphQL APIs to enable AI connectivity.

[18:52] - Model-Driven vs. In-Line Transformation — Why data transformation tools that reshape data in motion create scaling challenges, and how persistent data models solve enterprise-wide consistency.

[24:06] - AI Governance and Hallucination Prevention — Why deterministic data models are essential for trustworthy AI—Claude can't "make up" OEE numbers when the data graph dictates values.

[28:41] - Custom vs. Standard Data Models — Discussion of when to use ISA-95 accelerators versus custom models, using an automotive OEM wall-to-wall deployment as an example.

[33:46] - Red and Blue Namespace Architecture — How Fuuz balances enterprise governance with plant-level flexibility through extensible tenant-based data models.

[37:28] - What Category is Fuuz? — Craig explains how the platform spans MES, WMS, data ops, and application development—an operational intelligence layer, not a data lake.

[47:13] - Technical Architecture Deep Dive — Overview of Kubernetes backend, Node.js framework, RabbitMQ messaging, MongoDB with custom ORM, and the hybrid gateway for edge connectivity.

[51:16] - Real-World Deployments — Case studies including an automotive OEM running an entire car plant on Fuuz, Highbar Steel's solar-powered green steel mill, and PepsiCo co-packer integrations.

[53:52] - Advice for Getting Started — Craig's recommendation to define the problem first, assemble cross-functional IT/OT teams, and start small with the understanding that small problems often expose bigger ones.

"There's a reason why our color is purple, because if you mix red and blue together, it makes purple. We are the part that's in between—the highly structured enterprise data like ERP and PLM and the really unstructured data that's happening on the plant floor." — Craig Scott, CEO at Fuuz

"The ERP is a good source of truth for like the first 15 minutes that the data goes into the system, and then immediately, when you start generating real time data from the shop floor, it's out of date. Nothing is in sync anymore." — Craig Scott, CEO at Fuuz

"When I connect Claude to Fuuz, Claude can't make anything up. It can't imagine an OEE for my line or my machine because it's being dictated by our data graph." — Craig Scott, CEO at Fuuz

"I still look at AI as a tool, and I don't know that we're ready to acknowledge AI as the platform yet. We want to build tools and platforms that enable the technology, not rely on the new technology to be our platform." — Craig Scott, CEO at Fuuz

"Data is money, and if we can turn that data into actionable insights, now we can make more money for your business." — Craig Scott, CEO at Fuuz

Definition: A software layer that sits between operational technology (SCADA, historians, PLCs) and enterprise systems (ERP, PLM, CRM) to provide real-time data contextualization, persistence, and governance.

Why it matters: Traditional architectures leave a gap between shop floor data and business systems, causing data inconsistency and preventing AI from accessing trustworthy operational information.

Episode context: Craig describes Fuuz as the "shim" or "purple" layer that bridges red (OT) and blue (IT) data, enabling real-time synchronization and a true single source of truth.

Definition: An approach where data models are defined first as persistent, governed structures, and all systems read from and write to this single canonical model rather than transforming data in-line during transit.

Why it matters: In-line transformation tools work for small deployments but require re-implementation at every site. Model-driven persistence enables "once and done" enterprise-wide data consistency.

Episode context: Craig contrasted this with edge tools that reshape data in motion, explaining that persistent models scale across global enterprises with multiple ERPs and systems.

Definition: An architectural pattern that provides a single, hierarchical structure for all operational data, making it accessible to any system that needs it.

Why it matters: UNS is gaining adoption as a way to democratize data access, but without persistent contextualized state, it only provides current values—not the historical context needed for operations and AI.

Episode context: Craig acknowledged UNS as a great concept but emphasized that operational intelligence requires persistent state of contextualized data, not just real-time streaming.

Definition: A protocol that enables AI systems to connect to and understand data from external platforms through standardized interfaces.

Why it matters: MCP allows AI tools like Claude to access governed industrial data without requiring custom integrations or exposing companies to AI hallucination risks.

Episode context: Fuuz added MCP capability to expose their data graph to AI systems, ensuring AI outputs are governed by deterministic data rather than generating unreliable information.

Definition: An industry initiative working on standardized GraphQL APIs for industrial data exchange, enabling interoperability between industrial systems.

Why it matters: Standardized APIs reduce integration complexity and allow best-of-breed systems to share data through common interfaces.

Episode context: Craig mentioned Fuuz has been GraphQL-based since inception and is building an I3x connector to expose all enterprise system data through this emerging standard.

Craig Scott is the Founder and CEO of Fuuz, an industrial intelligence platform that bridges the gap between shop floor operations and enterprise systems. With a career spanning manufacturing engineering, owning tool-and-die and precision machining companies, consulting for Plex ERP, and founding a system integration business, Craig brings a unique perspective that combines hands-on manufacturing experience with deep technology expertise. He holds a degree in manufacturing engineering and a master's in manufacturing administration.

Q: What is the difference between a data lake and an operational intelligence platform?

A: A data lake stores denormalized data optimized for reporting and analytics but doesn't help run real-time operations. An operational intelligence platform provides persistent contextualized state and real-time event streaming, enabling both operational execution and analytics from a single governed source.

Q: Why do most manufacturing AI pilots fail to scale?

A: Most AI initiatives skip the foundational work of preparing and contextualizing data. Companies pipe raw data into data lakes or directly into AI tools without governance, leading to unreliable outputs. AI is only as good as the data it receives—without a persistent, governed data model, AI cannot produce trustworthy results at scale.

Q: Should manufacturers use standard data models like ISA-95 or build custom models?

A: Both approaches are valid depending on the use case. ISA-95-based accelerators work well for discrete manufacturing processes, but complex operations like automotive assembly with multiple lines require extensible custom models. The key is having a platform that allows starting with standards and extending as needed.

Q: How do you balance enterprise data governance with plant-level flexibility?

A: Implement governed core models at the enterprise level, then allow individual plants (as separate tenants) to extend those models with additional metadata. Plants can add context but cannot change underlying structures, preserving data integrity while enabling local adaptation.

Q: What should manufacturers do first to prepare for AI adoption?

A: Define the specific problem you're trying to solve and identify the value drivers behind it. Assemble a cross-functional team between IT and OT. Take inventory of your data sources and understand which hold the most valuable information. Start small—solving small problems often exposes bigger opportunities, and having the right platform in place makes solving those bigger problems easier.

Q: Can I use an industrial intelligence platform without replacing my existing ERP or MES?

A: Yes. Platforms like Fuuz are designed to integrate with existing systems rather than replace them. You can expose data from multiple ERPs, PLMs, and operational systems through a single governed layer, adding AI and modern API capabilities without wholesale system replacement.