For years, the airline industry’s ambition to modernise retailing was held back less by a lack of technology than by structural fragmentation. New tools were added to ageing systems, and individual functions were improved, but the broader commercial and operational chain remained disconnected.
That is now beginning to change.
The latest phase of AI adoption is pushing airlines to rethink not just individual processes, but the way decisions are made across the entire offer-to-delivery lifecycle. The shift is no longer about placing AI on top of isolated systems. It is about connecting data, decisions and outcomes from one end of the chain to the other.
That is how Alessandro Alfano, SVP Product & Technology at Accelya, describes the current turning point. In his view, adding AI capabilities to separate applications could only deliver limited benefits. The larger opportunity, he argues, lies in creating what he calls a central brain—an intelligence layer able to work across applications and communicate with each of them.
This approach addresses a longstanding weakness in airline operations. Traditionally, airlines have optimised offer creation, booking, settlement and delivery as separate stages. The result has often been a disconnect between commercial priorities and operational reality.
Applied end-to-end, AI begins to close that gap. Rather than improving one isolated decision at a time, it can assess how a change made upstream affects downstream outcomes across the whole chain.
That shift also has important implications for cargo.
Alfano points to the example of FLX AI Aviator, which can optimise load planning by incorporating order data from the passenger side. In practice, that means the system can identify when an aircraft is likely to operate close to full passenger load, recognise that belly cargo capacity will therefore be tighter, and adjust cargo forecasts accordingly.
For cargo operators, this introduces a much more predictive approach to capacity management. Instead of discovering constraints late in the process and reacting to them, airlines can anticipate them earlier and make more informed decisions on pricing, booking acceptance, and space allocation.
It also raises questions about traditional pricing logic. As capacity becomes more fluid and increasingly driven by live operational data, static cargo rate cards begin to look out of step with the way airlines actually manage their assets.
One of the most important implications of this AI-driven integration lies in the relationship between cargo and passenger operations. Even though both businesses depend on the same aircraft, they have rarely been optimised together in real time. That separation is now starting to weaken.
Still, adoption remains cautious. Airlines operate in a high-risk environment, and new technologies must prove both their value and their reliability before being entrusted with mission-critical processes. The pace of innovation may be accelerating, but airlines remain careful about how far and how fast they allow automation to influence core decisions.
Accelya’s own approach reflects that caution. Its AI tools are designed to remain fully controllable, allowing airlines to switch them off and fall back on manual workflows or business-rule-based logic whenever necessary.
The next step, however, is likely to go further than optimisation alone. Alfano points to the rise of agentic AI—AI capable of acting autonomously within defined boundaries. As he puts it, this is AI “with legs”: systems that do not simply analyse information, but can operate independently.
If that model matures, the implications for both passenger and cargo operations could be significant. Systems would not just react to demand or disruptions; they would anticipate them. They could generate offers, rebalance capacity, or respond to operational constraints before they become visible problems.
For cargo, that could open the door to a more proactive form of network management, where congestion, delay risk and capacity limitations are addressed earlier and more intelligently.
Perhaps the most meaningful change of all is the growing convergence between passenger and cargo business models. Cargo stands to benefit from retailing practices already established in the passenger world, including more advanced forms of dynamic pricing. At the same time, passenger operations may also have something to learn from cargo, particularly in the way capacity, yield and operational responsiveness are managed.
This is part of a broader structural evolution. Aircraft are no longer being viewed as separate passenger and cargo platforms, but increasingly as integrated capacity systems that need to be optimised as a whole.
