Marc Krüger-Sprengel
You just spent six months building a brilliant predictive maintenance model for Line 1. It works perfectly. The VP of Operations is thrilled and asks you to roll it out to Line 2 by next month.
And your heart sinks.
Because you know you cannot just copy and paste the model. Line 2 uses a completely different PLC and Scada. The data tags are named differently. The operators still use a different Excel sheet and notation for their shift notes. To deploy this to a second line, you have to re-wire the entire data pipeline from scratch.
This is exactly why 70% of manufacturing AI projects never leave pilot purgatory.
To escape this trap, you have to fundamentally change your architecture. You must decouple the application from the asset. This is what decision intelligence for manufacturing requires: a shared foundation, not another point solution.
You need a Context Layer.
What is a Context Layer?
It is a live, semantic graph that sits on top of the systems you already run. Instead of forcing your plant to conform to a rigid database schema, the context layer flexibly allows you to build data products as you need them and brings your local brownfield realities together.
Crucially, it doesn't just look at structured tag data. It links those raw alarms and machine states with unstructured data: the operator voice notes, the PDF SOPs, the handwritten whiteboard updates, and the maintenance photos, while seamlessly pulling in the enterprise context (material flow, quality specs) from the ERP.
How is this different from existing platforms?
When faced with this scaling problem, IT usually buys a generic data lake or a standard IIoT platform. Both fail for opposite reasons.
Standard IIoT platforms are great at piping raw machine telemetry (like Temp_Sensor_4), but they are completely blind to the enterprise context. They do not know what specific material batch is currently sitting in that machine.
Generic data lakes, on the other hand, just become dumping grounds. You throw all your tables into a central location, creating a "data swamp" that forces your data scientists to spend 80% of their time cleaning and joining data before they can write a single line of algorithmic code.
The Context Layer is different. It is purpose-built for manufacturing to fuse OT, IT, and operational knowledge out of the box.
When you build this foundation, it transforms how you deploy technology across the board.
Agentic AI & Context Serving
AI agents are only as good as the context they operate in. The Context Layer acts as the grounding engine, providing live context serving to agents so they can safely explain anomalies, recommend actions, and execute within strict, plant-specific guardrails.
Scalable AI and Custom Models
If you build a custom optimization model for Line 1, whether it is an algorithm to predict scrap rates or a machine learning tool to avoid unplanned downtime, you shouldn't have to re-engineer the entire data pipeline to deploy it to Line 2. Because your models rely on the semantic graph rather than hardcoded raw machine tags, your AI initiatives can scale seamlessly across your entire network.
Scalable Application Deployment
Instead of custom, point-to-point integrations for every new tool, the context layer enables scalable application deployment through declarative pipelines with as-needed context and data product serving. You can drop in bite-sized apps that work immediately across different machines and plants because the data abstraction is already handled.
An Open, Governed Ecosystem
This is where true enterprise scale happens. You aren't locked into a specific vendor's UI. The Context Layer exposes a clean, governed API that integrates with your existing landscape.
- Equip your in-house Data Scientists with immediate access to structured, production-ready data.
- Securely integrate external partners, legacy applications, or custom models via a governed API.
- Seamlessly push your mapped, contextualized data into Snowflake, stream it via Kafka, or visualize it in PowerBI.
Instead of spending six months cleaning data, teams can query the Context Layer for "Machine State + Operator Shift Notes + Material Batch for Product X" and deploy on day one. Governance, access rights, and safety guardrails are baked into the data layer, so central IT doesn't have to block experiments.
The Foundation for Decision Intelligence
The next era of manufacturing will not be defined by the companies with the biggest monolith, nor the one with the most app-builders. It will be defined by the companies with the highest decision velocity, and everyone else will be left behind. Stop treating your engineers as middleware. Give them a context layer, and watch how fast your local wins turn into global scale.
The Context Layer is a powerful scaling engine in its own right, solving the integration nightmares of today. But its true value lies in what it enables next: it scales the applications and AI of today, while unlocking the intelligence and agents of tomorrow as the foundation for decision intelligence for manufacturing.
The real prize comes when you unleash this intelligence across your network, where fully fused, contextualized data translates into ROI through scrap rate reduction, downtime remediation, and network-wide predictions for deliverability and Net Working Capital (NWC).
