How airlines are solving disruption in the AI era

Every revolution is an evolution—it is only the compression of time through history that makes change appear sudden.

To date, revolution has never moved at the speed of technology because new tech typically arrives in a world built for what it is trying to replace.

The telephone arrived in a world built around the telegraph. Infrastructure, established processes and human behavior all became barriers to adoption.

For a highly regulated industry like aviation—and particularly solving irregular operations—the artificial intelligence (AI) revolution is likely to evolve one use case at a time.

How far will airlines let it go?

At Grounded 2026 in Reykjavík, leaders from more than 35 airlines representing more than 40% of global passenger volumes gathered to discuss the future of passenger disruption management.

Question to airline leaders: If the technology worked tomorrow, how much decision-making would you actually hand over to AI during disruption?

Seventy-four percent were prepared to let AI take action of some kind, rather than simply make recommendations.

• 39% would allow AI to act within predefined rules.
• 28% would allow it to act autonomously, escalating exceptions.
• 26% wanted AI restricted to recommendations, with humans retaining the final decision.
• Just 7% were prepared to contemplate full autonomy for most passengers.

Clearly, the industry is not resisting AI. It appears ready to hand over meaningful decision-making but within boundaries.

Follow-up question: So, what is stopping airlines from implementing AI?

• 41% identified systems and data
• 27% organizational readiness
• 18% trust and governance
• Just 10% selected the business case and 4% regulation

In other words, 86% identified some form of technological, operational or organizational readiness as the primary constraint, rather than regulation or economics.

But the architecture is already shifting. Airlines may not have decided which models to use, which decisions to automate or how much autonomy to permit, but those questions are increasingly influencing the technology they buy today.

This is evident in every request for proposal (RFP) written in the last 18 months.

Why disruption is a demanding AI use case

Irregular operations combine many of the things AI appears increasingly good at with many of the things airlines find hardest to automate.

A single disruption can require an airline to consider everything from operational constraints and inventory to accessibility requirements and immigration restrictions—and then make decisions quickly for thousands of passengers at once.

AI potentially changes the economics of that complexity. More importantly, it promises to move beyond interpreting information to taking action. There is a profound difference between AI advising an agent and AI reaccommodating passengers directly.

Question to airline leaders: Where is the biggest gap between passenger expectations during disruption and what airlines can currently deliver?

• 41% chose speed of information
• 27% personal relevance
• 25% quality of options
• Just 8% chose human contact when it matters

This is important because the fundamental needs of a disrupted passenger haven't changed very much. What has changed is the acceptable interval between: Something has happened and tell me what it means for me. That interval is collapsing.

Flight trackers, social media, messaging groups—the airline is no longer the sole narrator of disruption. Sometimes the passenger knows first. AI compresses that interval further because once technology can understand a problem in seconds, passengers will increasingly wonder why resolving it still takes hours.

The comforting AI story—the one we would tell our kids at bedtime—follows a gentle trajectory of validation, regulation, public trust and gradual automation. Not quite the alarming rhetoric we hear from the AI Knights of the Round Table.

At Grounded, we asked airline leaders to consider a future in which a passenger's own AI agent handles rebooking, choices and compensation.

• 78% thought it would take years
• 15% considered it realistic in the near future
• Just 7% thought it imminent

They may well be right. But what if it happens faster?

Much of today's disruption model assumes the passenger comes to the airline for answers, chooses from the options the airline provides and relies on the airline to define what good recovery looks like. A personal AI agent would challenge all three.

The timeline is uncertain. But if the technological capability exists, the obvious question is: Why don't we simply hand over the problem?

The 1% problem

One recurring lesson from running airline disruption numbers is that small flight percentages translate into large passenger populations.

During the 2025 Thanksgiving period, 31 million passengers flew in the U.S. with a 99% completion rate—a modern logistical miracle. But the remaining 1% represents 310,000 passengers. This is the problem with describing failure as an error rate. At aviation-scale, edge cases represent populations.

Now apply the same logic to AI. Suppose an AI successfully resolves 10,000 disrupted passengers and makes materially wrong decisions for 100. Is that system successful? Of course, it depends on which 100 passengers.

A suboptimal hotel choice is one kind of error. Rebooking someone onto a journey they cannot legally complete is another. Failing to preserve an accessibility requirement or separating a family is different again.

Not all errors carry the same consequence.

AI also changes the reach of an error. A human agent might make a poor decision for one passenger. An automated system could potentially apply the same flawed reasoning to thousands before anyone notices.

A system that advises incorrectly therefore creates one kind of risk. A system authorized to act incorrectly creates another. For an AI advisor, accuracy may be a reasonable measure of success. But once AI is empowered to act, accuracy alone tells us precious little about the risk.

So, the more useful questions become: Which decisions are we prepared to automate? Which failures are we prepared to tolerate? And where must a human remain in the loop?

The truth is we don’t yet know.

Trust compounds

The AI revolution in airline disruption is already in motion. The more interesting discussion is how it arrives. The evidence suggests a progression where AI advises, recommends, acts within defined boundaries, then handles increasingly complex cases while escalating others.

Trust compounds one use case at a time. Each successful deployment becomes evidence for the next.

Interestingly, humans don't disappear in that model; their role changes. Instead of manually processing every passenger, they increasingly establish policy, supervise systems and intervene where the consequences of getting something wrong demand human accountability.

This is why the revolution already looks evolutionary. Viewed from inside the industry, the transformation feels remarkably incremental; the boundaries extending one automation at a time. Until one day the operating model bears little resemblance to the one we started with.