Industries
AI for Aviation & Aerospace
Aviation already knows how to govern systems that must not fail. That is an advantage and a complication: the sector has mature safety assurance, and AI does not slot neatly into frameworks built for deterministic systems whose behaviour can be specified in advance.
The position
Most AI in aviation today sits away from the flight deck - maintenance prediction, scheduling, ground operations, crew rostering, demand forecasting and passenger service. The consequences are still operational and, in the maintenance case, safety-adjacent enough that assurance matters.
We help organizations classify AI by proximity to safety, apply governance proportionate to that classification, and produce documentation that sits alongside an existing safety management system rather than competing with it.
What is pressing
Four pressures specific to this sector.
01
Safety proximity classification
The first task is honest categorisation: which systems are safety-critical, which are safety-adjacent, and which are purely commercial. Treating all three alike produces governance nobody follows.
02
Assurance for non-deterministic systems
Existing certification practice assumes specified behaviour. Models that generalise need a different evidence base - validation conditions, monitoring, drift detection and a defined fallback.
03
Maintenance and airworthiness records
Predictive maintenance changes when work happens. Where a model influences an airworthiness-relevant decision, the reasoning has to be recorded to the standard the record itself is held to.
04
Operational data across borders
Aviation data moves across jurisdictions continuously. Processing location, retention and access rights need to be settled at contract stage rather than discovered during an audit.
Where the divisions apply
Which divisions this sector uses, and in what order.
Listed most relevant first. Most organizations in this sector enter through the first and draw on the others later.
03
AI Audit
Prove the AI you already run does what you say it does.
Read the division02
AI Governance
Build your AI governance framework before regulation forces you to.
Read the division01
AI Strategy
Know where AI creates value in your organization before you spend on it.
Read the division06
Learning and Development
Send your board into an AI decision able to ask the right questions.
Read the divisionQuestions
What this sector asks first.
- Is AI allowed in safety-critical aviation systems?
- That is a certification question for the relevant authority, and the bar is appropriately high. Most organizations we work with are not attempting it - their AI sits in maintenance, operations and commercial functions, where the governance question is real but the certification question is not.
- We have a safety management system already. Is that enough?
- It is the right foundation and the right home for this work. What it usually lacks is treatment of systems whose behaviour is learned rather than specified - validation evidence, drift monitoring and a defined fallback. Those extend the system rather than replacing it.
- Where should an aviation organization start?
- A classification exercise: every AI system in use, sorted by how close it sits to a safety outcome. It is a short piece of work and it determines everything after it, because governance effort should be concentrated where consequence is, not spread evenly.