NEOM Bay Airport Digital Strategy
AI-driven operational intelligence for a greenfield international airport.
An AI-first operating design for a greenfield international airport. Passenger flow prediction, baggage optimisation and integrated operations control were all designed before the airport opened. The prediction models were tested in simulation against similar airports. The measurement framework was built so real operating data sharpens their accuracy from day one.
NEOM Bay Airport was a greenfield build. There were no legacy systems to work around, but also no historical data to train the first models. That opened a chance to design AI into the airport's operations before it opened, instead of retrofitting AI into existing systems later.
Designed a digital twin architecture for airport operations, covering passenger flow, baggage handling and airside management. Simulated training data from comparable airports. Set the measurement framework so real operating data would check and improve the predictions from the first day of operations.
The architecture for operational intelligence arrived on schedule. Passenger flow prediction models were validated against simulation data, and the baggage optimisation system cut projected handling time by 18%. The architecture carried no legacy limits to design around, so later NEOM infrastructure builds used it as their starting point.
- My role
- Designed the digital operations architecture and its measurement framework, with Meinhardt
Building the right measurement framework before you need the predictions is worth more than bolting AI onto data collected for other purposes. The same holds for a growing business. What you start measuring today decides 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.
Journey Transformation →●Live Demo: AI Predictive Maintenance →The same diagnostic logic from deployments like this one, applied to your business in half a day.
Book the WorkshopDiscuss a similar programme- NEOM: publicly announced project; Bay Airport is a confirmed component of the NEOM development programme
- Employer testimonial letter from Meinhardt SUIT Pte Ltd (Managing Director, July 2025) naming this project available to procurement teams on request; not published online
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No historical data, no passengers yet. Just simulation, and a bet that the framework would hold once real traffic started moving through a terminal that doesn't exist yet. The team argued hard over how far to trust data from comparable airports versus waiting for our own. We built the measurement architecture first and designed it to be corrected by real data once the airport is running. I still think about that sequencing decision every time a client wants predictions before they've decided what to measure. Get the order right up front and the rest gets easier.
