NEOM Bay Airport Digital Strategy
AI-driven operational intelligence for a greenfield international airport.
NEOM Bay Airport was a greenfield build, which meant no legacy systems to integrate around but also no historical data to train initial models. The opportunity was to design an AI-first operational architecture before the airport opened, rather than retrofitting AI into existing infrastructure.
Designed a digital twin architecture for airport operations, covering passenger flow, baggage handling, and airside management. Simulated training data from comparable airports. Defined the measurement framework so real operational data would validate and improve predictions from day one of operations.
Operational intelligence architecture delivered on schedule. Passenger flow prediction models validated against simulation data. Baggage optimisation system reduced projected handling time by 18% in simulation. Zero-legacy architecture became the reference model for future NEOM infrastructure builds.
Building the right measurement framework before you need the predictions is worth more than retrofitting AI onto data collected for other purposes. The same applies to a growing business: what you start measuring today determines what AI can do for you in three years. Design the data collection first.
Measurement-Before-Prediction Architecture Framework
The data architecture decision sequence used for the NEOM Bay Airport deployment: how to design the measurement framework and data collection schema before prediction models are built, so real operational data improves accuracy from day one. Covers the five design decisions that determine whether AI predictions will be actionable at launch or require six to twelve months of data collection to become usable.
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