For years, airline revenue management has been one of the most sophisticated commercial functions in any industry. Complex, data-heavy, and constantly evolving.
But what we are seeing now is different.
AI is not just improving revenue management. It is redefining what it actually is.
And for CIOs, CTOs, and Heads of Commercial, the question is no longer whether AI matters. It is how quickly your organisation can operationalise it without creating more complexity than value.
From optimisation to autonomy
Traditional revenue management systems were built on optimisation. Forecast demand, adjust availability, manage fare classes.
AI shifts that model towards autonomy.
Instead of analysts constantly adjusting parameters, modern AI-driven systems are learning continuously. They ingest real-time signals, adapt pricing dynamically, and improve without manual intervention.
This was highlighted in the podcast conversation, where the guest described building an ancillary revenue solution that could:
- Operate within defined guardrails
- Learn from data automatically
- Reduce the need for constant human correction
That last point matters more than it seems.
Because the biggest bottleneck in revenue management today is not data. It is human bandwidth.
Airlines are dealing with increasing complexity across channels, products, and customer expectations. AI is not just about better pricing. It is about scaling decision-making.
Niels Colemont
One of the biggest shifts has been moving from manual optimisation to systems that can learn by themselves. When we started building an ancillary revenue solution, the goal was clear. We did not want users spending hours correcting forecasts. We wanted a system that could operate within defined limits, learn from data, and improve continuously. At the same time, airlines need control. Setting the right boundaries is critical.
AI should not replace expertise. It should amplify it. And as we move further into Offer and Order, this balance between automation and control will only become more important.
The overlooked opportunity in ancillary revenue
One of the most interesting insights from the podcast is how late the industry was to apply advanced revenue management to ancillaries.
Flights have been optimised for decades.
Ancillaries have not.
The guest described a situation where there was:
- No clear visibility on ancillary performance
- No structured pricing strategy
- No capability for dynamic pricing
This gap led directly to the creation of an AI-driven ancillary pricing solution.
And this is still where many airlines are today.
Ancillary revenue is often treated as an add-on rather than a core revenue stream. But with AI, that changes quickly.
Dynamic bundling, personalised offers, and real-time pricing are now becoming viable at scale. Especially as airlines move further into Offer and Order environments.
This is where we are seeing a sharp increase in hiring demand across airline tech. Teams need people who understand both legacy revenue management and modern retailing logic.
Ai and the shift to offer and order
If NDC was the bridge, Offer and Order is the destination.
And AI is the engine that makes it commercially viable.
In a traditional environment, pricing is constrained by filed fares and booking classes. In an Offer and Order world, airlines can construct offers dynamically.
That includes:
- Price
- Product
- Bundle
- Timing
- Channel
AI enables this to happen in real time.
Recent industry discussions, including updates from IATA’s World Passenger Symposium, have shown increasing momentum around interoperability between systems. Airlines and vendors are starting to demonstrate how different components of the retail stack can work together.
This matters because AI does not operate in isolation.
It needs:
- Clean data
- Connected systems
- Clear commercial logic
Without that, AI simply accelerates confusion.
The real challenge is not technology
There is a common assumption that AI adoption is a technology problem.
It is not.
It is a translation problem.
The same issue we see across OOSD programmes applies here. Legacy systems, future state ambitions, and operational realities must align.
Otherwise, progress stalls.
From a recruitment perspective at Thornton Gregory, this is where the biggest gap sits.
Airlines are not struggling to find people who understand AI.
They are struggling to find people who can:
- Bridge legacy RM systems and modern retail platforms
- Translate commercial needs into technical requirements
- Operate across Offer, Order, and pricing logic
Because AI increases output, but it also increases noise.
The people who stand out are the ones who bring clarity and judgement.
Speed, scale, and decision-making
Another key shift AI introduces is speed.
In traditional RM, decisions are often batch-based. Daily updates, scheduled adjustments.
AI moves this towards continuous decision-making.
That creates both opportunity and risk.
Opportunity because airlines can respond instantly to:
- Demand fluctuations
- Competitor pricing
- Customer behaviour
Risk because poor logic scales just as quickly.
This is why the concept of guardrails, mentioned in the podcast, is so important.
AI should not replace control. It should enhance it.
The best implementations are not fully autonomous. They are structured systems where:
- Humans define strategy
- AI executes within boundaries
- Feedback loops continuously refine outcomes
Culture still matters
One of the more understated themes from the podcast is culture.
The airline industry, despite its complexity, is still driven by people who enjoy working together, challenging ideas, and pushing innovation forward.
That becomes even more important with AI.
Because adopting AI requires:
- Willingness to question existing processes
- Openness to change
- Ability to collaborate across teams
At the same time, there is a balance.
As the guest put it, it is important to challenge the status quo. But it is equally important to understand why things are the way they are.
Airline systems are complex for a reason.
AI does not remove that complexity. It sits on top of it.
What this means for hiring leaders
For hiring managers across airline tech, the implications are clear.
The next phase of revenue management is not just about better tools. It is about better people.
Specifically, people who can:
- Connect AI capabilities to commercial outcomes
- Navigate both legacy and modern architectures
- Drive implementation, not just strategy
We are seeing increased demand for hybrid profiles. Individuals who are part commercial thinker, part technologist, part translator.
And that demand is only going to increase as AI becomes more embedded in core airline systems.
Looking ahead
AI in revenue management is still early.
Yes, there are live implementations. Yes, there are proven use cases.
But we are just at the beginning of what is possible.
Over the next few years, we will likely see:
- Fully dynamic offer creation across all channels
- Deeper personalisation at scale
- Greater interoperability between vendor systems
- Increased reliance on AI for real-time decisioning
The airlines that succeed will not be the ones with the most advanced algorithms.
They will be the ones who can integrate AI into their commercial strategy without losing control or clarity.
