How to Start an Industrial AI Project
A practical step-by-step guide for choosing, scoping, and launching an Industrial AI project in a factory.
Executive summary
The best Industrial AI projects do not start with algorithms.
They start with a real operational problem, a clear decision, available data, and people who will use the output.
This guide explains how to start an Industrial AI project without wasting time on technology that does not create value.
Step 1: Choose a painful problem
Start with a problem that matters operationally.
Examples include:
- Repeated equipment failures
- Poor quality stability
- High energy consumption
- Slow reporting
- Frequent stoppages
- Unclear process drivers
- Manual troubleshooting
- Who will use the output?
- What will they do differently?
- How often will they use it?
- What happens if the model is wrong?
- What is the cost of inaction?
- Historian data
- PLC/SCADA data
- Lab results
- Maintenance history
- Downtime events
- Production records
- Inspection findings
- Operator logs
- Trend chart
- SQL summary
- Rule-based alert
- Correlation analysis
- Simple regression
- Manual dashboard
- Wrong tags
- Shutdown periods
- Product changes
- Maintenance events
- Sensor issues
- Practical constraints
- Downtime reduction
- Earlier failure detection
- Lower prediction error
- Faster reporting
- Fewer quality deviations
- Lower energy consumption
- Higher maintenance planning accuracy
- Dashboard or app
- Alerting logic
- User workflow
- Ownership
- Monitoring
- Retraining plan
- Documentation
Avoid vague goals such as "use AI in the plant."
Step 2: Define the decision
Every AI project should support a decision.
Ask:
If no decision changes, the AI system is not useful.
Step 3: Check data availability
Identify the required data sources:
Do not assume data is usable because it exists.
Step 4: Build a baseline
Before machine learning, build a simple baseline.
Examples:
A baseline tells you whether advanced modeling is justified.
Step 5: Involve domain experts
Industrial AI requires process knowledge.
Domain experts help identify:
Without experts, the model can learn misleading patterns.
Step 6: Define success metrics
Examples include:
Success should be measurable.
Step 7: Plan deployment
A model is not complete when it works in a notebook.
Deployment requires:
Summary
A good Industrial AI project starts small, proves value, and becomes part of the workflow.
The winning sequence is:
Problem → Decision → Data → Baseline → Model → Workflow → Improvement