Building AI-Ready Data Infrastructure in Industrial Software

You want to add AI to your industrial software. But when you look at what you have to work with, the reality is sobering: dozens of PCs running different software versions, inconsistent logging formats, and data scattered across isolated systems that have never been designed to talk to each other.

This is the starting point for most industrial AI initiatives—not clean datasets ready for training, but fragmented operational data that needs significant work before any AI system can use it.

The Reality of Industrial Data

What You Typically Have

In most industrial software environments, the data landscape looks something like this:

Data Type	Characteristics	AI Readiness
Application logs	Unstructured, inconsistent formats	Low
Error logs	Valuable but sparse	Medium
Equipment status	Real-time but ephemeral	Low
User interactions	Often not captured	Very Low
Process outcomes	May exist in separate systems	Variable

The fundamental challenge: data exists, but not in forms that AI systems can readily consume.

The Heterogeneity Problem

Each workstation in your fleet might have:

• Different software versions with different logging formats
• Different hardware configurations affecting performance baselines
• Different usage patterns based on operators and workflows
• Different network conditions (some air-gapped, some connected)

This heterogeneity isn’t a bug—it’s the nature of industrial deployments. Any AI strategy must account for it.

What Data Should You Collect?

Before collecting everything possible, consider what questions you want AI to answer:

Operational Intelligence

Goal: Understand how systems are being used and identify optimization opportunities.

Data Category	What to Capture	Why It Matters
Session patterns	Start/end times, duration, idle periods	Usage optimization
Feature usage	Which functions are used, frequency, sequences	UX improvement
Error frequency	Types, timing, recovery patterns	Reliability improvement
Performance metrics	Response times, resource usage	Performance optimization

Predictive Maintenance

Goal: Anticipate equipment issues before they cause failures.

Data Category	What to Capture	Why It Matters
Equipment telemetry	Temperature, vibration, cycle counts	Failure prediction
Consumable tracking	Usage rates, replacement history	Inventory optimization
Error patterns	Pre-failure indicators, degradation signs	Early warning
Environmental factors	Ambient conditions, power quality	Root cause analysis

Process Optimization

Goal: Improve experimental or manufacturing outcomes.

Data Category	What to Capture	Why It Matters
Process parameters	Settings, configurations, recipes	Outcome correlation
Results/outcomes	Success rates, quality metrics	Process improvement
Timing data	Duration, delays, bottlenecks	Efficiency optimization
Operator actions	Interventions, adjustments	Best practice identification

Data Collection Architecture

The Structured Logging Approach

Moving from ad-hoc logging to structured, AI-ready data capture:

Before (Typical Log Entry):

2025-12-26 10:23:45 INFO: Started process for sample ABC123
2025-12-26 10:24:12 WARN: Temperature slightly elevated
2025-12-26 10:45:33 INFO: Process completed successfully

After (Structured Event):

{
  "timestamp": "2025-12-26T10:23:45Z",
  "event_type": "process_start",
  "session_id": "sess_abc123",
  "sample_id": "ABC123",
  "equipment_id": "incubator_01",
  "parameters": {
    "target_temp": 37.0,
    "duration_min": 120
  },
  "context": {
    "software_version": "2.4.1",
    "operator_id": "op_jane"
  }
}

Key Principles

1. Event-Driven Capture

Instead of periodic snapshots, capture events as they occur:

Event Type	Trigger	Data Captured
State changes	Equipment status transitions	Previous/new state, duration
User actions	Button clicks, selections	Action type, context, timing
Process milestones	Start, checkpoint, completion	Parameters, measurements
Anomalies	Threshold violations, errors	Conditions, severity, context

2. Contextual Enrichment

Every event should carry enough context to be meaningful in isolation:

• Who: Operator, system, or automated process
• What: Specific action or state change
• Where: Equipment, workstation, location
• When: Precise timestamp with timezone
• Why: Triggering condition or intent (when available)

3. Consistent Schema

Define schemas that work across your heterogeneous environment:

Base Event Schema:
├── timestamp (ISO 8601)
├── event_type (enumerated)
├── source
│   ├── equipment_id
│   ├── software_version
│   └── site_id
├── payload (event-specific)
└── metadata
    ├── session_id
    └── correlation_id

Data Pipeline for Air-Gapped Systems

For environments without continuous connectivity, a store-and-forward approach:

Local Collection Layer

Each workstation maintains:

Component	Purpose	Implementation
Event buffer	Temporary storage	SQLite or embedded DB
Schema validator	Data quality	JSON Schema validation
Compression	Efficient storage	GZIP or LZ4
Export scheduler	Periodic extraction	USB or network sync

Aggregation Layer

Central system receives and processes:

Component	Purpose	Implementation
Data ingestion	Receive from multiple sources	Message queue or batch import
Deduplication	Handle retransmissions	Event ID tracking
Normalization	Handle version differences	Schema evolution rules
Storage	Long-term retention	Time-series DB or data lake

Analysis Layer

Where AI actually operates:

Component	Purpose	Implementation
Feature extraction	Prepare for ML	Batch or streaming pipelines
Model training	Build predictive models	Offline training infrastructure
Inference	Generate predictions	Edge or central deployment
Feedback loop	Capture outcomes	Labeled data collection

AI Capabilities by Data Maturity

Your AI ambitions should match your data maturity:

Level 1: Basic Logs Only

Available AI Capabilities:

Capability	What It Does	Data Required
Anomaly detection	Flag unusual patterns	Time-series logs
Log clustering	Group similar events	Unstructured logs
Error prediction	Anticipate failures	Error history

Practical Applications:

• Alert when system behavior deviates from normal
• Automatically categorize and prioritize errors
• Identify patterns preceding failures

Level 2: Structured Events

Additional AI Capabilities:

Capability	What It Does	Data Required
Usage analytics	Understand user behavior	Structured events
Process mining	Map actual workflows	Event sequences
Recommendation	Suggest next actions	User history

Practical Applications:

• Identify most/least used features
• Discover inefficient workflow patterns
• Personalize interface based on user role

Level 3: Rich Context + Outcomes

Advanced AI Capabilities:

Capability	What It Does	Data Required
Predictive maintenance	Forecast equipment issues	Telemetry + failure history
Process optimization	Recommend parameters	Settings + outcomes
Quality prediction	Forecast results	Full process data

Practical Applications:

• Schedule maintenance before failures occur
• Suggest optimal process parameters
• Predict batch success probability

AI Applications for User Experience

Intelligent Assistance

Feature	Description	User Benefit
Smart defaults	Pre-fill based on context	Reduced setup time
Autocomplete	Suggest completions	Faster data entry
Error prevention	Warn before mistakes	Fewer errors
Contextual help	Relevant documentation	Self-service support

Implementation Approach:

• Start with rule-based systems using collected patterns
• Graduate to ML models as data accumulates
• Use small LLMs for natural language interfaces

Workflow Optimization

Feature	Description	User Benefit
Task prioritization	Suggest order of operations	Efficiency
Resource allocation	Optimize equipment usage	Throughput
Schedule optimization	Plan maintenance windows	Uptime
Bottleneck identification	Highlight constraints	Process improvement

Predictive Insights

Feature	Description	User Benefit
Completion estimates	Predict finish times	Planning
Quality forecasts	Early warning of issues	Intervention opportunity
Capacity planning	Anticipate resource needs	Proactive management
Trend analysis	Identify gradual changes	Early action

Current State of the Art (2025)

Small Language Models for Edge Deployment

For industrial settings, local inference is often necessary:

Model	Size	Capability	Memory
Qwen3-0.6B	0.6B	Basic reasoning	~0.5GB
Phi-4	14B	Strong reasoning	~7GB (4-bit)
Llama 3.2	1-3B	General purpose	1-2GB
Mistral 7B	7B	Efficient inference	~4GB (4-bit)

Use Cases:

• Natural language queries against system data
• Automated report generation
• Intelligent error explanation

Time-Series Analysis

Approach	Best For	Maturity
Statistical (ARIMA, Prophet)	Seasonal patterns	Production-ready
Deep Learning (Transformers)	Complex patterns	Emerging
Foundation Models (TimesFM, Chronos)	Zero-shot forecasting	Research stage

Use Cases:

• Equipment degradation prediction
• Demand forecasting
• Anomaly detection

Computer Vision

Model Type	Size	Capability
YOLO variants	5-50MB	Object detection
MobileNet	10-20MB	Classification
Small VLMs (2-4B)	1-2GB	Visual Q&A

Use Cases:

• Equipment status monitoring
• Quality inspection
• Document/display reading

Implementation Roadmap

Phase 1: Foundation

Focus: Establish data collection infrastructure

Task	Deliverable
Define event schema	Documented data model
Implement structured logging	Updated logging framework
Set up local storage	SQLite or similar on each node
Create export mechanism	USB or network sync capability

Phase 2: Aggregation

Focus: Centralize and normalize data

Task	Deliverable
Deploy central data store	Time-series database
Build ingestion pipeline	Automated data import
Implement data quality checks	Validation and alerting
Create basic dashboards	Visibility into collected data

Phase 3: Initial AI

Focus: Deploy first AI capabilities

Task	Deliverable
Anomaly detection	Automated alerts for unusual patterns
Usage analytics	Reports on feature utilization
Error classification	Automated categorization
Basic predictions	Simple forecasting models

Phase 4: Advanced AI

Focus: Sophisticated AI applications

Task	Deliverable
Predictive maintenance	Equipment failure forecasting
Process optimization	Parameter recommendations
Natural language interface	Conversational queries
Feedback integration	Continuous model improvement

Key Considerations

Data Privacy and Security

• What data can be collected? (Regulatory constraints)
• How is sensitive data handled? (Anonymization, access control)
• Where can data be stored? (Jurisdiction, air-gap requirements)
• Who owns the data? (Customer vs. vendor)

Technical Debt Management

• How will schema changes be handled?
• What’s the strategy for version compatibility?
• How are historical data migrations managed?
• What’s the data retention policy?

Organizational Readiness

• Who owns the data infrastructure?
• What skills are needed? (Data engineering, ML ops)
• How are AI decisions validated?
• What’s the feedback mechanism for model quality?

Closing Thoughts

Building AI capabilities in industrial software isn’t primarily a machine learning problem—it’s a data engineering problem. The sophisticated models exist; the challenge is creating the data foundation they need to be useful.

Start with the data you have (logs), structure it properly, and build incrementally. Each level of data maturity unlocks new AI capabilities. Don’t try to jump to advanced predictive models before you have the data infrastructure to support them.

The good news: with proper instrumentation today, you’re building the foundation for AI capabilities that will mature alongside the rapidly improving models. The data you collect now will become increasingly valuable as AI technology advances.

This represents a practical approach based on real-world industrial software constraints. Specific implementations will vary based on your regulatory environment, existing infrastructure, and organizational capabilities.

---

Further Reading:

• MLOps: Continuous delivery for machine learning
• Time Series Forecasting Best Practices
• Data Engineering on Azure
• Edge AI Deployment Patterns