The Data Gap for Industrial AI
The promise of AI in industrial environments - predictive maintenance, quality optimization, autonomous control - depends entirely on data quality. Not data volume. Not model sophistication. Data quality.
Most industrial AI projects fail not because the models are wrong, but because the data is wrong. Missing timestamps make time-series models unreliable. Absent quality indicators mean models train on bad data. Lack of operational context means models cannot distinguish between a sensor reading and a calibration event.
What AI Actually Needs
Industrial AI systems require:
Semantic context. Every data point must carry meaning - what asset it belongs to, where it sits in the operational hierarchy, what unit of measure it uses, and what quality indicator the source protocol assigned.
Temporal consistency. Timestamps must be acquired at the source with consistent precision. AI models that correlate across data streams need synchronized timing, not arrival-time approximations.
Complete history. Training data with gaps produces models with blind spots. The data plane must guarantee completeness through buffering and replay.
Deterministic delivery. Inference pipelines that receive data with variable latency produce unreliable results. The data path must have bounded, predictable timing.
Edge vs Cloud Execution
Closed-loop control requires edge execution. A cloud round-trip adds 50-500ms of latency. For quality inspection, robotic control, or safety-critical decisions, this latency is unacceptable.
The data plane must support co-located processing: AI inference workloads running on the same edge hardware as data acquisition, with no network dependency in the critical path.
The Data Plane Role
The data plane does not run AI models. It provides the foundation that makes industrial AI possible: structured data with context, deterministic delivery with bounded latency, complete history with no gaps, and a runtime that supports co-located compute. Without this foundation, industrial AI remains a proof-of-concept.