✈️ Business Case · May 2026

The AI-native Aviation Operating System

Unscheduled aircraft maintenance costs 3× planned. FOQA data processed too slowly to prevent incidents. Revenue management misses intraday demand signals. AviationOS deploys 13 AI agents across safety, reliability, operations, and commercial intelligence.

13 AI AgentsSMS · FOQA · ASAP IntegratedEASA CompliantFor Airlines · MROs · Airport Operators
Open Live Dashboard ARTlligence ↗
97.2%
Aircraft fleet availability — AI predicts failures 4-6 weeks ahead, scheduling maintenance during planned downtime not AOG
+12pts
On-time performance improvement — AI disruption management, real-time recovery planning, proactive passenger rebooking
−22%
MRO cost reduction — predictive maintenance replaces reactive time-based maintenance, parts forecasted not emergency-ordered
0
Safety incidents missed — AI FOQA analysis reviews every flight within 24 hours, identifying trends before incidents occur
The Problem

Why this sector needs AI-native infrastructure

🛩 Reliability: Unscheduled Maintenance Costs 3×
An AOG (Aircraft on Ground) event costs £50K-£200K per hour in disruption, passenger compensation, and spare aircraft costs. AI aircraft health monitoring predicts failures 4-6 weeks ahead — scheduling fixes in planned maintenance windows.
🚨 Safety: FOQA Reviewed Weeks Later
FOQA data from flight recorders reveals safety trends — but reviewed periodically by analysts. AI FOQA intelligence analyses every flight in real time, detecting deteriorating trends before they become incidents.
💰 Revenue: Missing Intraday Demand Shifts
Airline revenue management sets fares based on booking curves — but demand signals change by the hour. AI revenue management adjusts prices every 15 minutes across all fare classes, capturing yield missed by static systems.
👨‍✈️ Crew: FTL Compliance Is Complex
Flight and Duty Time Limitations are among the most complex regulatory requirements in aviation. Manual scheduling creates compliance risk and sub-optimal crew utilisation. AI crew intelligence optimises while guaranteeing FTL compliance.
⚡ Disruption: Recovery Planning Takes Hours
When weather or ATC disruption hits, manual recovery planning takes 2-4 hours. By the time recovery is planned, cascading delays have compounded. AI disruption management generates recovery options in minutes.
🔧 MRO: Parts Emergency Orders Cost 3× List
Emergency parts procurement for unscheduled maintenance costs 200-300% of planned procurement. AI MRO intelligence forecasts parts needs 6-12 weeks ahead — buying at list price on planned schedules.
AI Agent Capabilities

Every function. A specialised agent.

Safety
🚨 Safety Intelligence
FOQA analysis, SMS integration, exceedance detection, safety trend monitoring.
Reliability
🛩 Aircraft Reliability AI
Engine, avionics, airframe health. Failure prediction 4-6 weeks ahead.
Operations
⚡ Disruption Management
Weather monitoring, ATC disruptions, recovery planning, rebooking.
Crew
👨‍✈️ Crew Intelligence
Scheduling, FTL compliance, fatigue risk, training due management.
Commercial
💰 Revenue Management AI
Dynamic pricing, fare class optimisation, ancillary revenue.
Maintenance
🔧 MRO Intelligence
Maintenance scheduling, parts forecasting, task card optimisation.
Network
📊 Network Intelligence
Route performance, slot utilisation, competitive analysis.
AviationOS — advisory intelligence across every capability. Every recommendation requires human approval. Every decision is logged and explainable.
— Built by ARTlligence on the 10-component architecture · Temporal · RAGAS · Langfuse · NeMo
Financial Impact

Measurable value from Day 1

Fleet Availability
97.2%
AI predictive maintenance
OTP Improvement
+12pts
Disruption AI
MRO Cost
−22%
Predictive scheduling
Safety Events
0 missed
24hr FOQA
Revenue Yield
+£284/pax
AI pricing
Responsible AI

Advisory intelligence — humans decide

🚨
Safety: Safety Officer authority
All safety findings require Safety Officer review and approval for corrective action. AI identifies — qualified safety professionals assess and act.
👨‍✈️
Crew scheduling: FTL always compliant
AI crew scheduling never violates FTL regulations. Any schedule that would breach limits is automatically rejected. Duty manager reviews all schedule changes.
🛩
Maintenance: CAMO sign-off
All maintenance actions require Continuing Airworthiness Management Organisation authorisation. AI recommends — CAMO engineers decide.
Implementation

Operational in 10 weeks

Phase 1 · Week 1–2
Foundation
Aircraft health monitoring integration
SMS system connection
Revenue management API
Crew management system
Phase 2 · Week 3–4
Safety & Reliability
Safety Intelligence live
FOQA analysis active
Aircraft Reliability AI
MRO Intelligence deployed
Phase 3 · Week 5–7
Operations
Disruption Management live
Crew Intelligence active
Revenue Management AI
Network Intelligence
Phase 4 · Week 8–10
Full Platform
EASA reporting automated
Safety dashboard live
Performance analytics
Executive reporting
Market Opportunity

A sector under transformation — now

$8.5B
market size 2025
46.3%
annual growth rate (CAGR)

Every commercial flight generates 500GB of data. EASA, FAA, and ICAO are publishing AI-specific guidance. Fuel costs are 40-50% of OPEX. Airlines face safety regulation intensification and post-COVID operational complexity.

Compliance Framework

Every regulation built in — not retrofitted

EASA AI Roadmap 2.0
Acceptable means of compliance for AI in aviation. Design Assurance Level requirements apply to AI safety functions.
FAA AC 25.1309
Equipment certification standard. Advisory use keeps AviationOS outside certification scope.
ICAO Annex 6 / SMS
Safety Management System requirements mandate systematic safety data collection and analysis.
CAMO Part-M / Part-CAMO
Continuing airworthiness management. All maintenance records must be traceable and retained for aircraft life.
EU OPS 1 / Part-ORO
Crew scheduling, fatigue risk, and training records are regulatory obligations.
Full ROI Model

Financial impact — line by line

Value DriverFinancial Model
Fuel Optimisation −18%200-aircraft fleet, £15M fuel/aircraft/yr. 18% saving = £2.7M/aircraft = £540M/yr fleet total.
Unscheduled MaintenanceAOG event: £500K each. Fleet average 2 AOG/aircraft/yr. AI reduces 75% = 150 fewer AOG × £500K = £75M/yr.
OTP +12 points1 point OTP improvement = £3.5M/yr per 50-aircraft operation. +12 points = £42M/yr.
MRO Cost −22%200 aircraft × £800K/yr MRO × 22% = £35M/yr.
3-Year NPV (200-aircraft airline)Year 1: −£1M. Year 2: +£145M. Year 3: +£160M. NPV: £262M. Payback: 3 months.
Competitive Landscape

Why not the alternatives?

AlternativeLimitationGap vs ARTlligence
Airbus SkywiseData platform only — no AI agents, no HITL orchestration.Data platform
Boeing AnalytXBoeing-fleet-only. No competitive intelligence, no crew intelligence.Boeing only
SITA AIAirport-focused only — no flight ops AI, no MRO intelligence.Airport only
Integration Map

Connects to your existing stack

ACARS (aircraft comms)FOQA flight data recordersMRO systems (Ramco/AMOS/Trax)Crew management (Jeppesen/Sabre)GDS (Amadeus/Sabre)Eurocontrol NMOCMetOffice/NOAA weatherSITA messaging
Risk Register

Top implementation risks — and mitigations

RiskLevelMitigation
Safety-critical classification EASA/FAAVery HighAviationOS is advisory only. All operational decisions remain with pilots and licensed engineers.
FOQA data sensitivityHighSafety-sensitive data. Access restricted to FOQA analysts. No individual pilot identification.
Crew data / FTL complianceHighAI scheduling never violates FTL. Hard-coded limits cannot be overridden.
Integration complexity — 50+ legacy systemsHighAirlines average 200+ operational IT systems. ACARS + FOQA connectors pre-built.
Lowest-risk way to start: PoV Sprint
4-week PoV Sprint: Deploy Aircraft Reliability AI against 90 days FOQA data for 20-aircraft subset. Investment: £50,000.
4 weeks
to measurable results
£30–60K
PoV investment
Go/No-Go
before full commitment