# Airport Software Market ## Market Structure Roughly 900 airports globally handle 84 million aircraft movements per year across commercial aviation. Total aviation IT spend runs approximately $50 billion annually, with $10 billion in optimization and AI. The airport-specific BI/AI segment is roughly $250 million today, growing fast. The market segments by tier. Tier 1 hubs (the Heathrows, Changi, CDGs) have large IT budgets and complex, mature operations. They often build bespoke systems or buy from the legacy incumbents. Tier 2 and 3 airports (roughly 750 of the 900) handle 47+ million movements per year collectively but individually lack the budgets, IT teams, or organizational maturity for bespoke solutions. This tier is where SaaS-based, modular, pay-as-you-grow platforms have the highest product-market fit. The same dynamic plays out in [[Industrial AI MOC]]: the largest customers can afford bespoke, the mid-market needs productized. ## Competitive Landscape The airport software market maps onto a two-axis framework: **Axis 1: Legacy vs. Innovative.** Legacy players (SITA, Amadeus, Honeywell) have deep install bases, long-term contracts, and proven reliability. They also carry technical debt, slow iteration cycles, and monolithic architectures. Innovative entrants offer modern tech stacks (microservices, AI-native, SaaS) but must prove reliability and earn trust. **Axis 2: Rigid/Expensive vs. Agile/Affordable.** The incumbents sell enterprise-grade solutions at enterprise-grade prices with multi-year implementation timelines. Agile entrants offer modular, configurable platforms with lower upfront costs and faster deployment. The tension: airports need enterprise reliability but want startup agility. The most dangerous quadrant for incumbents: agile and innovative players that can demonstrate compliance with aviation standards (A-CDM EUROCONTROL spec, ICAO ASBU alignment) while offering modern architecture and affordable pricing. The legacy players' defense is [[Incumbent Bundling Risk]]: ship a "good enough" optimizer bundled with the existing AODB stack at zero incremental cost. The counterargument: deep optimization requires vendor-neutrality (sitting above all equipment and systems) and a pace of iteration that legacy vendors structurally cannot match. See [[Technical Moat Assessment Framework]]. Notable players: Veovo (airport intelligence), Leidos (airside operations), PASSUR Aerospace (aviation analytics), ADB SAFEGATE (gate and airfield), Copenhagen Optimization (flow and resource management). Each occupies a different slice of the value chain. ## Deployment Dynamics Airport AI has the same structural challenge as all [[Industrial AI MOC]]: every airport is different. Different terminal layouts, different airline mixes, different ground handler contracts, different legacy systems. The [[Bespoke Engineering in Industrial AI]] problem is acute. The question for any optimization vendor: can you deploy at airport #3 faster than airport #1? If not, you're a consultancy. See [[Deployment Velocity]] and [[Consultancy-to-Platform Transition]]. [[Wright's Law]] should apply to deployment learning curves. If it doesn't, the platform claim is hollow. Channel strategy matters: geographic scale in airport tech usually comes through local integration partners, not direct sales. Every region has regulatory nuances, language requirements, and incumbent relationships that a centralized sales team cannot navigate alone. ## The Natural Expansion Path Vendors entering airport operations typically follow a predictable expansion sequence that mirrors the airport maturity model: A-CDM first (the beachhead: milestone tracking, information sharing, departure planning). Then [[Pre-Departure Sequencer]] (optimizes departure queue beyond basic A-CDM). Then Turnaround Management System (granular visibility into ground handling task completion). Then an Information Broker or integration middleware layer (connects the expanding data sources). Then [[Total Airport Management]] (unified model across all domains). Then an Airport Operations Management System (the full decision-support layer with KPIs, alerting, and post-ops analytics). Each module expands the data footprint and the stakeholder touchpoints, which increases switching costs and enables [[Land-and-Expand in Enterprise AI]] dynamics. A vendor that starts with A-CDM and proves value has a natural path to 5-10x ACV expansion through module addition. The question is whether each expansion genuinely adds value or just adds features. Related: [[Airport Operations MOC]], [[Industrial AI MOC]], [[Deployment Velocity]], [[Consultancy-to-Platform Transition]], [[Bespoke Engineering in Industrial AI]], [[Incumbent Bundling Risk]], [[Technical Moat Assessment Framework]], [[Land-and-Expand in Enterprise AI]] --- Tags: #deeptech #systems #kp