Why Industrial AI Is Its Own Discipline
Physics is a prior, not a feature. Industrial processes obey conservation laws, thermodynamics, electromagnetics, reaction kinetics. A model trained to ignore those priors is a model that will generalise badly and be rejected by the engineers whose trust it needs.
Uncertainty is not a nice-to-have. A setpoint recommendation without a confidence interval, an anomaly alert without a probability calibration, a forecast without documented prediction bounds — all fail the basic requirement of operational decision-making under consequence.
Data is scarce, non-stationary, and regulated. Industrial data is rarely clean, labelled, or representative of rare events. The data governance an industrial AI programme requires is a separate engineering discipline from the modelling itself.
Regulatory gravity has arrived. The EU AI Act imposes classification-driven obligations; IEC 62443 governs AI integration into OT; ISO/IEC 42001 is now the de facto AI management system standard.
Use-Case Domains We Cover
Predictive maintenance — vibration, thermal, acoustic, electrical-signature, multimodal fault prediction
Process optimisation — yield, throughput, energy-per-unit, setpoint optimisation under safety and quality constraints
Anomaly detection — unsupervised and weakly-supervised detection on process telemetry
Quality prediction and inspection — defect detection, inline quality scoring, machine-vision integration
Energy and grid forecasting — load, generation, price, balancing-reserve forecasting
Demand forecasting under industrial constraint — capacity-, contract-, and lead-time-constrained prediction
Digital twin integration — AI components within calibrated physics-based twins; surrogate modelling
Safety-adjacent decision support — advisory-only systems engineered against IEC 61508 / 61511 separation principles
Autonomous process control — closed-loop AI control, scoped carefully against safety envelope and regulatory regime
Generative AI for industrial operations — knowledge extraction from O&M documentation, structured report generation, operator-assist interfaces.
AI Strategy, Use-Case Selection & Value Framework
- Industrial-AI strategy aligned to operational, financial, sustainability, and regulatory objectives
- Use-case catalogue and decision-value screening with quantified counterfactual against current practice
- Data-readiness assessment: sensor density, data quality, historian architecture, labelling availability, governance posture
- Build-vs-buy-vs-partner framework for each use case
- Investment-committee and board-grade business case for AI-programme capital request
Data Architecture for Industrial AI
- Industrial data platform: historian (Aspen IP.21, OSIsoft PI/AVEVA PI, Honeywell PHD, GE Proficy), time-series DB (InfluxDB, TimescaleDB), unified namespace (Sparkplug B, HighByte, Litmus), lakehouse (Databricks, Snowflake, Iceberg/Delta/Hudi)
- Data lineage, quality, and provenance engineering
- Sensor coverage gap analysis and instrumentation strategy
- Edge-to-cloud data architecture: latency, bandwidth, sovereignty, offline-operation trade-offs
Model Engineering: Physics-Informed & Hybrid Approaches
- Physics-informed neural networks (PINN), physics-constrained learning, hybrid first-principles-plus-ML architectures
- Classical and tabular methods where they outperform deep learning
- Time-series methods: state-space models, transformer-based forecasters, ensemble approaches
- Computer vision for inspection, machine-vision integration, hardware-accelerated inference on edge devices
- Foundation-model integration scoped carefully against hallucination risk, IP boundaries, data-residency
Uncertainty Quantification & Model Trustworthiness
- Bayesian methods, deep ensembles, conformal prediction, calibration-verification methodologies
- Explainability strategy: SHAP, LIME, feature attribution, counterfactual explanation matched to audience
- Out-of-distribution detection and safety-net architecture
- Adversarial robustness assessment where threat model warrants
- Documentation of model assumptions, limitations, and failure modes