Severe winter weather sweeping across the United States has once again placed the global airline industry under intense strain. Airport closures, aircraft groundings, frozen infrastructure, and cascading delays have disrupted flight schedules not only across North America, but worldwide, as aircraft and crew rotations ripple through tightly interconnected networks.
Moments like these are when airline operations are tested most severely. Decisions must be made in minutes, safety margins cannot be compromised, and communication volumes surge dramatically. Millions of passengers expect timely, accurate updates while frontline staff juggle weather data, air traffic constraints, crew availability, and aircraft readiness — all in real time.
Increasingly, airlines are turning to artificial intelligence to help them operate through this chaos. Generative AI and data-driven decision systems are no longer confined to innovation labs or marketing pilots. Instead, they are becoming embedded into the operational core of airlines, especially during irregular operations triggered by extreme weather.
The current cold snap has highlighted how AI is shifting from an experimental tool to a practical necessity.
Irregular Operations: Where Airlines Feel the Pressure Most
Weather disruption has always been one of aviation’s hardest challenges. Unlike mechanical faults or staffing issues, weather events are external, fast-moving, and unpredictable. A single storm system can shut down a major hub, creating knock-on effects across dozens of routes and continents.
During these periods, airlines face two parallel pressures:
- Operational decision-making — rerouting aircraft, reassigning crews, managing airport congestion, and ensuring regulatory compliance.
- Customer communication — handling massive surges in passenger inquiries while maintaining clarity, empathy, and brand consistency.
Traditionally, these challenges have required large teams, manual coordination, and scripted responses that struggle to keep pace with fast-changing conditions. AI is increasingly being used to bridge this gap — accelerating decisions without removing human oversight.
Air France-KLM’s AI Factory: Building Scale Into Innovation
One of the most structured approaches to enterprise AI adoption in aviation comes from Air France-KLM. Rather than deploying isolated AI tools, the group has invested in a cloud-based generative AI “factory” designed to support development and deployment across the organisation.
The AI factory was built in partnership with Accenture and Google Cloud, following earlier work to migrate core airline applications to the cloud. That foundation proved critical. Without modern infrastructure, AI systems struggle to access data reliably or operate at scale.
The airline group has described the factory as a way to standardise how AI solutions are built, tested, governed, and reused. Instead of reinventing processes for each department, teams can draw from shared components, models, and security frameworks.
This approach has already delivered measurable outcomes across:
- Ground operations
- Engineering and maintenance
- Customer-facing services
According to the partners involved, enterprise-wide AI deployment has accelerated development speed by more than 35%, allowing ideas to move from prototype to production far more quickly than before.
From Cloud Migration to AI-Assisted Maintenance
The AI factory builds on years of preparatory work. Prior to launching large-scale generative AI initiatives, Air France-KLM focused on migrating legacy systems to the cloud — a move that enabled better data integration and faster experimentation.
Since then, the group has developed a private AI assistant and retrieval-augmented generation (RAG) tools that connect large language models to internal documentation and operational data. These tools are already being used to support tasks such as diagnosing aircraft damage and assisting with repair processes.
Instead of replacing engineers or technicians, AI acts as an accelerator — surfacing relevant documentation, previous cases, and procedural guidance in seconds. This reduces downtime, improves consistency, and helps less experienced staff access institutional knowledge more easily.
Importantly, these tools operate within strict governance boundaries, ensuring sensitive operational data remains secure.
Training Employees to Work With AI, Not Around It
A critical part of Air France-KLM’s strategy has been workforce enablement. Employees are actively trained on how to use AI tools effectively, responsibly, and safely.
Rather than limiting AI access to technical teams, the airline has focused on giving staff across departments the skills needed to apply large language models to real business challenges. This democratisation of AI capability helps ensure that innovation is driven by operational needs, not just technical curiosity.
The result is an organisation that can react faster during disruption — a capability that becomes invaluable during extreme weather events like the current cold snap.
United Airlines: Using AI to Shorten Decision Cycles
United Airlines has taken a similarly pragmatic approach, focusing on how AI can reduce response times during irregular operations.
In an interview with CIO.com, United’s CIO Jason Birnbaum described AI as a tool to “shorten decision cycles” when flights are delayed or cancelled — exactly the scenario airlines face during severe winter weather.
United’s AI journey began with customer communications, one of the most visible pain points during disruption. When thousands of flights are affected, customer service teams face an overwhelming volume of inquiries, all while passengers expect timely, personalised updates.
Preserving Brand Voice During Disruption
United places strong emphasis on its communication style through its “Every Flight Has A Story” programme. Customer service representatives — internally referred to as “storytellers” — are trained to communicate with empathy, clarity, and consistency.
However, during extended periods of disruption, maintaining that standard becomes difficult.
As Birnbaum explained, it is simply not feasible for humans to manually craft unique messages for every disrupted flight at scale. AI was introduced to help prioritise and draft messages for the most impactful situations.
The process involves feeding the AI model with:
- Core flight data
- Real-time operational updates
- Internal communications between crews, gate agents, and operations teams
- External data such as weather conditions
Using this information, the AI generates a draft message that customer service teams can review and refine before sending.
Teaching AI How United Communicates
One of the biggest challenges was not teaching AI to understand flight data, but teaching it how United communicates.
Prompt engineering played a key role in aligning AI-generated messages with the airline’s tone and values. Safety, for example, is always emphasised — but without alarming passengers unnecessarily.
The AI system is learning to choose words carefully, reflecting how United wants to reassure customers while remaining transparent.
Birnbaum noted that AI also excels at contextual awareness. By referencing historical flight data, the system can explain delays more clearly than human agents often do — information that passengers consistently find helpful.
Industry-Wide AI Maturity: Progress, but Uneven
Despite high-profile use cases, the airline industry as a whole remains at a mid-level of AI maturity.
According to analysis from Boston Consulting Group, airlines are currently rated as “average” in terms of AI readiness, having improved from slightly below average in the previous year. Out of 36 airlines surveyed, only one met the highest criteria for being fully prepared for an AI-enabled future.
The gap between leaders and laggards is widening.
BCG’s research suggests that by 2030, airlines that embed AI deeply into their workflows could achieve operating margins 5 to 6 percentage points higher than competitors that rely on more traditional processes.
AI Moves Into the Operational Core
Looking ahead, generative AI is expected to become embedded into the operational core of both airlines and airports.
Key areas include:
- Schedule optimisation
- Crew allocation
- Aircraft rotation planning
- Passenger rebooking and recovery
- Disruption forecasting
These decisions must be made quickly, often with incomplete information, and always within strict safety and regulatory boundaries.
Microsoft has stated that data-driven AI systems can reduce the root causes of flight delays by up to 35% by improving how disruptions are forecast and managed. By anticipating bottlenecks earlier, airlines can limit how widely disruptions spread across their networks.
Revenue and Cost Impacts Are Already Emerging
Beyond operational resilience, AI is also delivering measurable financial benefits.
According to Microsoft, airlines using AI-driven personalisation tools are seeing revenue increases of approximately 10% to 15% per passenger. These gains come from better offers, targeted upgrades, and more relevant ancillary services.
At the same time, AI-powered self-service tools — including chatbots and automated rebooking systems — are helping airlines reduce customer service costs by up to 30%.
During weather disruption, these tools become especially valuable, absorbing demand spikes that would otherwise overwhelm call centres and airport staff.
Why Weather Events Accelerate AI Adoption
Extreme weather events act as forcing functions. They expose weaknesses in manual processes and highlight the limitations of legacy systems.
The current cold snap underscores a broader shift underway in aviation: AI is no longer a “nice to have.” It is becoming an essential layer that supports resilience, safety, and customer trust during periods of stress.
Airlines that have invested early in cloud infrastructure, data integration, and governance are now better positioned to deploy AI when it matters most.
Those that have not may find themselves struggling to keep pace — not just operationally, but competitively.
Human Oversight Remains Central
Despite growing automation, airlines are clear that AI is not replacing human judgment. Safety-critical decisions remain firmly in human hands.
Instead, AI functions as an amplifier — surfacing insights faster, drafting communications more efficiently, and helping teams prioritise their attention where it matters most.
During severe weather, this collaboration between humans and machines can mean the difference between controlled disruption and systemic breakdown.
A Glimpse of Aviation’s Future
As climate volatility increases and passenger expectations rise, irregular operations may become more frequent, not less. Airlines that rely solely on manual coordination will find it increasingly difficult to cope.
The proactive use of AI during the current cold snap offers a glimpse of aviation’s future: one where data-driven systems support rapid decision-making, personalised communication, and operational resilience — even when conditions are at their worst.
For the airline industry, AI is no longer just about efficiency. It is becoming a core capability for surviving disruption and maintaining trust in an unpredictable world.