Everyone in manufacturing is chasing the promise of AI: predictive maintenance, real-time quality control, energy optimization, and even autonomous operations. But despite the momentum, most industrial AI initiatives never reach full deployment. Studies show up to 90% of industrial AI projects stall before scaling across sites. Why? Because the problem isn’t your AI models. It’s the data foundation beneath them.
Launching an AI project is easy. Scaling it across dozens of plants is where manufacturers hit a wall.
Every site runs on different PLCs, protocols, and systems, forcing teams to rebuild integrations from scratch
Data lives in silos, fragmented and inconsistent across equipment and sites
Legacy infrastructure can’t handle modern edge-to-cloud workflows
IT and OT teams often operate on separate islands, creating gaps in governance and collaboration
And when models finally run, they’re starved for context or limited to test environments that can’t scale
The outcome is predictable: stalled progress, wasted investment, and another “AI initiative” that never sees production.
AI in manufacturing succeeds or fails based on one thing: data quality and accessibility. Most factories run on hundreds of machines and sensors, each producing data in its own format and cadence. Without a consistent way to connect and contextualize it all, every AI project becomes a custom integration nightmare.
That’s the missing link in most Industrial AI strategies:
A unified, governed OT data foundation that connects every system and makes data usable anywhere.
With a single data layer, manufacturers can finally move from isolated experiments to enterprise-wide execution—transforming raw signals into contextualized, AI-ready data pipelines that flow seamlessly from edge to cloud.
To move beyond proof-of-concept AI, manufacturers need to adopt a data-first approach, not an AI-first mindset (that will follow).
Here’s what that looks like in practice:
- 1.
Connect every data source: PLCs, SCADA, historians, MES, and sensors without relying on custom code or middleware.
- 2.
Standardize and contextualize data so it’s analytics-ready from the moment it’s captured.
- 3.
Govern centrally with secure access controls, unified schemas, and consistent definitions across IT and OT.
- 4.
Deploy AI where it adds value: at the edge for real-time inference, in the cloud for enterprise analytics, or anywhere in between.
- 5.
Scale with confidence using containerized applications and centralized edge management to replicate success across multiple sites.
When this foundation is in place, AI becomes easier to operationalize, driving measurable outcomes like:
Improved OEE and productivity
Fewer unplanned shutdowns
AI-driven quality improvements
Real-time visibility across every facility
Forward-thinking manufacturers are already proving what’s possible when you start with the right data foundation.
By standardizing OT data with Litmus Edge, companies have:
Deployed 90 sites in just six months, unlocking full enterprise visibility
Reduced downtime and maintenance costs with predictive insights
Improved quality with AI-driven anomaly detection
Powered cloud-scale analytics and digital twins that accelerate innovation
These are the manufacturers turning data chaos into competitive advantage — and scaling AI without the headaches.
Industrial AI will define the next decade of competitiveness. But success won’t come from experimenting with new models—it will come from fixing the foundation that feeds them. That’s the difference between AI that scales and AI that fails.
October 29, 2025 at 11 AM ET
In this live session, we’ll break down:
Why most AI projects stall before scaling
The architecture behind a true data-first foundation
How to enable AI at the edge, in the cloud, and across every site
Real-world success stories from global manufacturers
If you’re ready to make AI work across your entire enterprise, this is the session you don’t want to miss: Register now for free