AI IN MANUFACTURING
Industrial AI applies machine learning, computer vision, language models, and AI agents to manufacturing operations. It runs on operational technology (OT) data from PLCs, SCADA systems, and historians —data that
must be connected, contextualized, and governed before any model output can be trusted. Litmus builds that Industrial Data Foundation once, then activates AI on it at the asset, site, and enterprise levels.
standardized industrial data architecture
optimized packing and volume tracking
Real-time equipment monitoring across sites
Real-time monitoring and corrective action
The economics of Industrial AI changed in 2026. LLM inference costs fell far enough that use cases which once cost millions of dollars a year to run now cost thousands, and models can hold context across long, multi-step tasks.
The plant has not changed nearly as fast. OT data still sits across PLCs, SCADA systems, historians, and other control systems. It arrives without enough context, and its structure varies from site to site.
Litmus closes that gap. Connect, contextualize, and govern OT data once in the Industrial Data Foundation, then activate AI on it anywhere—at the machine, at the site, or across the enterprise.
TWO TYPES OF AI
Industrial AI divides into two types with different latency budgets, deployment models, and data requirements: Operational AI, which runs at the edge in real time, and Enterprise AI, which runs in the cloud across sites.
The LLM shift added the first without replacing the second. Most stalled AI programs picked one and assumed it covered everything.
OPERATIONAL AI
Real-time, localized, prompt-driven
Runs at the edge, next to the machine
ENTERPRISE AI
Continuous, high-compute, inference-driven
Runs in the cloud across all sites
The two types need different data, in different shapes, delivered to different systems—from the same governed source. That source is the Industrial Data Foundation.
ASSET · SITE · ENTERPRISE
Industrial AI operates across three layers—asset, site, and enterprise—each with different users, latency requirements, and data needs.
Choosing an AI strategy means choosing which layer you are solving for first. Most manufacturers will eventually need all three.
