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AI USE CASE
Flight Delay Prediction and Proactive Rebooking
Predict flight delays before they happen and automatically rebook affected passengers to minimise disruption.
Data you need
Historical flight performance records, real-time weather feeds, air traffic control data, and aircraft maintenance schedules covering at least 12–24 months.
Required systems
- erp
- data warehouse
Why it works
- Real-time data pipelines ingesting weather, ATC, and maintenance feeds with latency under 15 minutes.
- Direct API integration with the GDS or airline reservation system to automate rebooking offers.
- A dedicated model monitoring process that triggers retraining when prediction accuracy drops below a defined threshold.
- Clear escalation rules defining when the system acts autonomously versus when an agent must approve the rebook.
How this goes wrong
- Insufficient historical delay data or inconsistent maintenance logs degrade model accuracy below useful thresholds.
- Rebooking logic is not integrated with the GDS or inventory system, making proactive reaccommodation manual and slow.
- Model performs well in training but drifts seasonally as weather patterns and route networks change without retraining.
- Passenger notification channels are fragmented, so predictions are acted on too late to provide a meaningful alternative.
When NOT to do this
Do not attempt this if your operation handles fewer than 50 flights per day, the data volume is too low to train reliable delay models and the ROI does not justify the infrastructure investment.
Example vendors
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