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Use Cases

Where data, ML and AI move the needle in aviation.

A working catalogue of the workflows we build — drawn from airline OCCs, airport apron control, MRO bays and finance back-offices. Use it as a menu, a starting point, or a benchmark against your own roadmap.

Airport Operations & Computer VisionPassenger Experience & Ancillary RevenueRevenue Management & PricingFleet, Maintenance & EngineeringNetwork, Crew & Operations ControlFinance, FP&A & Revenue AccountingAirport Commercial & Non-Aeronautical
01

Airport Operations & Computer Vision

Cameras, lidar and edge ML are turning the apron and terminal into a real-time operating system — eliminating manual radio calls and shaving minutes off every turn.

Autonomous jet bridge & passenger boarding tunnel docking

Problem
Today an operator manually drives the jet bridge to a parked aircraft, costing 3–6 minutes per turn and creating safety incidents.
Approach
Stereo cameras + computer vision detect aircraft type, door position and fuselage curvature, then drive the bridge automatically with sub-centimetre precision. ML models trained on thousands of dockings handle weather, lighting and unusual aircraft attitudes.
Impact
2–4 minutes saved per turn, near-zero contact incidents, frees ground staff for higher-value work. Heathrow T2 and Schiphol have piloted variants reporting >90% autonomy rates.

Edge GPUs · YOLO/Detectron · ROS · digital twin simulation

Apron safety & FOD (foreign object debris) detection

Problem
A single bolt on the runway can cause a $200M incident (Concorde, 2000). Manual sweeps miss objects and waste runway capacity.
Approach
Always-on cameras with object detection scan taxiways and gates, alerting ground control within seconds. Models distinguish debris from shadows, water and routine equipment.
Impact
Runway closure time reduced by 40–60%, insurance premiums reduced, regulatory compliance.

Multi-camera CV · anomaly detection · real-time alerting

Queue prediction & dynamic security lane staffing

Problem
Static security rosters create 30+ minute waits at peaks and idle staff at troughs.
Approach
Computer vision counts heads in queues, ML forecasts arrivals 90 minutes out using flight schedules, weather and historic patterns. Workforce systems auto-rebalance lane openings.
Impact
Average wait −35%, staff cost −12%, measurable lift in retail dwell-time downstream.

Time-series forecasting · CV crowd density · WFM integration

Baggage tracking with vision + RFID fusion

Problem
Mishandled bag rate sits at ~7 per 1,000 passengers globally — costing the industry $2.3B per year.
Approach
RFID tags read at every conveyor junction are fused with overhead vision to detect mis-routes the moment they happen, not at the carousel.
Impact
Mishandling −60%, recovery time halved, IATA Resolution 753 compliance built-in.

Stream processing · RFID · CV · graph routing

02

Passenger Experience & Ancillary Revenue

When passengers trust the app, they leave the gate. Every extra minute spent in the terminal converts to F&B and retail spend.

Personalised real-time boarding intelligence

Problem
Anxious passengers camp at the gate 60+ minutes early, missing ancillary spend opportunities at bars, lounges and shops.
Approach
ML model fuses live boarding-pace data, walk-time from passenger location, security wait, and historic gate behaviour to push a single trustworthy 'leave-by' notification. The app reassures passengers that they will not be left behind.
Impact
Pilot data from a major European hub: +18% F&B spend per enplaned passenger, +9 NPS, gate-area congestion −25%.

Indoor positioning · Bayesian ETA · push orchestration · mobile app

Dynamic ancillary offer engine

Problem
Bag fees, seat upgrades and lounge passes are sold at flat prices regardless of context.
Approach
Reinforcement-learning pricing engine prices ancillaries per passenger, per channel, per moment — taking into account load factor, fare class, loyalty tier and propensity-to-buy.
Impact
Ancillary revenue per passenger +6–11% within 6 months of deployment.

Contextual bandits · feature store · A/B platform

Disruption recovery for passengers

Problem
When a flight cancels, call centres are overwhelmed and passengers receive inconsistent rebooking offers.
Approach
AI agent ranks every feasible rebooking using passenger value, MCT, partner availability and EU261/APPR exposure, then offers options proactively in-app before the passenger even calls.
Impact
Compensation cost −22%, contact-centre volume −40%, CSAT during disruption +30 points.

OR solvers · LLM agents · regulatory rule engine

03

Revenue Management & Pricing

The original AI use case in aviation — modernised with deep learning and causal inference.

Deep-learning dynamic pricing

Problem
Legacy RM systems (PROS, Sabre) rely on EMSR heuristics and miss substitution effects across origin-destinations.
Approach
Sequence models forecast willingness-to-pay per O&D, incorporating competitor scrapes, search-to-book funnels and macro signals. Re-prices every 5 minutes inside guardrails.
Impact
RASK uplift of 1.5–3.5% — typically 8-figure annual contribution at network carrier scale.

Transformers · competitor scraping · GDS integration

Passenger segmentation & lifetime-value modelling

Problem
Loyalty programs treat the top 5% well and the remaining 95% identically.
Approach
Embedding-based segmentation clusters passengers by behaviour rather than tier, enabling targeted offers, churn-save campaigns and corporate-account expansion.
Impact
Marketing efficiency 2–3×, churn in mid-tier −15%.

Vector DB · uplift modelling · CDP

04

Fleet, Maintenance & Engineering

Sensors on a single widebody generate 1TB+ per flight. Most carriers use less than 5%.

Predictive maintenance & AOG avoidance

Problem
Unscheduled removals cost $10k–$150k per event before counting passenger disruption.
Approach
Anomaly-detection on QAR/ACMS sensor streams flags component degradation 50–500 flight-hours before failure. Recommendations are routed into the MRO planning system.
Impact
AOG events −30 to 50%, parts inventory −12%.

Time-series ML · survival analysis · MRO integration (AMOS, TRAX)

Engine health monitoring & replacement planning

Problem
Engine shop visits are the single largest maintenance cost line. Bad timing wastes residual life or risks dispatch reliability.
Approach
Digital twin combines EGT margin, oil consumption and route mix to optimise the next-shop-visit decision per tail and per engine position.
Impact
Avg shop-visit interval extended 8–12%, six- to seven-figure savings per engine over life.

Physics-informed ML · digital twin · IFS planning

Fuel burn optimisation

Problem
Fuel is 25–35% of an airline's cost base. Crews historically lacked closed-loop feedback on burn behaviour.
Approach
Per-flight, per-tail, per-pilot models recommend cost-index, climb profile and idle-reverse usage. Coaching is delivered in EFB.
Impact
0.8–2.5% fuel saved — at $80/bbl that's $20–60M/yr for a 100-aircraft fleet, plus material CO₂ reduction.

ML on FDM data · EFB integration · sustainability reporting

05

Network, Crew & Operations Control

OCC decisions made in seconds set the cost base for the next 24 hours.

Crew sickness recovery & auto-callout

Problem
If a crew member calls in sick 90 minutes before push-back, dispatchers manually phone reserves — often delaying or cancelling the flight.
Approach
Event-driven system detects the sickness call, ranks every legal reserve crew member by location, duty limits and qualifications, sends a push-and-call cascade, and auto-rebuilds the pairing if no reserve responds within SLA.
Impact
Crew-driven delays −45%, dispatcher workload halved, complete audit trail for FRMS.

OR optimisation · workflow engine · telephony API

Disruption recovery & schedule re-optimisation

Problem
Storms or ATC restrictions cascade for days because recovery is solved one flight at a time.
Approach
Mixed-integer optimiser re-solves the network in minutes — minimising passenger disruption, crew break-rules and fuel cost simultaneously.
Impact
Recovery time −60%, EU261 exposure −20%.

MILP solvers · CPLEX/Gurobi · live ops data feeds

Stand & gate allocation

Problem
Manual stand planning leaves widebodies on remote stands and burns ground-handling minutes.
Approach
Constraint solver re-optimises every 15 minutes using live ETAs, towing time, MARS-stand compatibility and passenger walking distance.
Impact
Bus-gate usage −30%, on-time performance +1.5pp.

Constraint programming · A-CDM integration

06

Finance, FP&A & Revenue Accounting

The CFO function is the next aviation frontier — interline settlement, rolling forecasts and audit are ripe for automation.

AI-driven rolling FP&A

Problem
Quarterly forecasts are stale by week 3. Variance analysis takes a fortnight.
Approach
ML forecasts revenue, fuel cost, maintenance and labour at daily granularity. LLM agents draft variance commentary and route exceptions to owners.
Impact
Forecast cycle 14d → 1d, accuracy +20–30%, finance team redeployed to partnering.

Probabilistic forecasting · LLM agents · EPM integration (Anaplan, Pigment)

Interline & revenue-accounting automation

Problem
Prorate disputes and uplift errors leak 0.5–1.5% of passenger revenue.
Approach
ML reconciliation flags anomalies in coupon, ACM/ADM and IDEC files; LLM agents draft dispute letters and prepare BSP submissions.
Impact
Leakage −60%, revenue accountants freed for exception work.

Document AI · graph matching · workflow

Fraud, chargeback & fare-abuse detection

Problem
Carded fraud and hidden-city ticketing cost majors mid-eight-figures annually.
Approach
Graph-neural-network model spots booking rings and unusual itinerary topologies in near real time.
Impact
Chargebacks −35%, false-positive rate halved.

GNN · streaming features · 3DS2 integration

07

Airport Commercial & Non-Aeronautical

Non-aero typically delivers 40%+ of airport EBITDA. Most of it is still managed on spreadsheets.

Retail catchment & concession optimisation

Problem
Tenants pay flat MAGs that ignore actual passenger flow per terminal zone.
Approach
Footfall analytics + flight-mix data inform tenant mix, lease terms and dynamic concession pricing.
Impact
Concession revenue per pax +8–14%, vacancy halved.

Indoor positioning · GIS · BI

Car park dynamic pricing

Problem
Static pricing leaves capacity unsold midweek and turns customers away on Fridays.
Approach
ML demand forecast + RL pricing optimises rates per product, per dwell-day, per booking lead-time.
Impact
Park revenue +10–18% within one season.

Demand forecasting · RL pricing · booking engine

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