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NEOM Bay Airport Digital Strategy

AI-driven operational intelligence for a greenfield international airport.

AI-first operational architecture for a greenfield international airport: passenger flow prediction, baggage optimisation, and integrated operations control designed before the airport opened. Prediction models simulation-validated against comparable airports, with the measurement framework built so real operational data sharpens accuracy from day one rather than after months of live data collection.

The Challenge

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.

The Approach

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.

The Outcome

Operational intelligence architecture delivered on schedule. Passenger flow prediction models validated against simulation data, and the baggage optimisation system cut projected handling time by 18%. Because the architecture carried no legacy constraints to design around, later NEOM infrastructure builds used it as their starting point rather than beginning from scratch.

SME Takeaway

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.

Architecture Schematic

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.

Journey Transformation →●Live Demo: AI Predictive Maintenance →

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External References & Verification

NEOM: publicly announced project; Bay Airport is a confirmed component of the NEOM development programme

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