AI-Assisted Workflows: Where It Works (and Where It Doesn't)

What Works

Predictive maintenance: Sensors predict failures weeks ahead. 20-30% cost reduction, 15-25% downtime reduction.

Quality detection: Computer vision identifies defects better than humans. 5-15% detection improvement, 30-50% time savings.

Production scheduling: AI optimizes sequences across machines. 5-20% throughput, 10-15% setup reduction.

Demand forecasting: ML predicts demand better than historical averages. 10-30% accuracy improvement, 15-25% inventory reduction.

What Struggles

Highly variable products: Custom items need engineering judgment, not pattern matching. ML models need thousands of examples; you have dozens.

Troubleshooting: Requires understanding your specific context. AI trained on generic data misses facility-specific factors.

Safety decisions: Can't use probabilistic AI where errors injure people. Human expertise is non-negotiable.

Strategic decisions: Future conditions are unpredictable. Scenario planning beats historical data analysis.

The Framework

Ask: Can AI access the signal? Is there a clear right answer? Is there enough data? What's the cost of error? Will humans actually review recommendations?

If the answers are yes, AI can help. Otherwise, use domain expertise.