What problem does it solve?
Disruption is when an airline's service is tested hardest. A storm, a strike or an air traffic control restriction can cancel many flights at once, and large numbers of passengers then call, queue at transfer desks and post on social media at the same time, most asking the same three questions: what happened, what are my options, and what am I entitled to. Contact centres are sized for a normal day, so waiting times explode exactly when anxiety is highest, and the passengers who most need a person (families, passengers with reduced mobility, long haul connections) wait behind everyone else.
Most of the work is rule bound. The airline already knows which passengers are affected, which alternative flights have seats, what the fare rules allow and, in many markets, what care and compensation the law requires. First generation chatbots could only point to a web page. The step change is an agent that is connected to the reservation and departure control systems, can present and confirm real options, issue a refund or voucher, and explain the reason in plain language, while staying inside the airline's reaccommodation policy and passenger rights rules.
How does it work?
- Detect and notify. When operations change a flight, the agent (or staff drafting with AI, as at United) sends a message by app, SMS or email that explains what changed and why, before the passenger has to ask.
- Authenticate and load the trip. The passenger opens the conversation from the message or the app; the agent identifies the booking, the connections and the loyalty status, so nobody has to search for a reservation.
- Offer real options. The agent pulls the airline's reaccommodation offer and alternatives with confirmed seats, standby options, a refund or an eCredit, and explains the fare rules and the care the passenger is entitled to (meals, hotel, transport).
- Act through approved tools. The passenger picks an option; the agent confirms the new flight, issues the refund request, voucher or eCredit and shows where the bags are, through a small allow list of reservation, ticketing and baggage actions.
- Hand over well. Complex itineraries, group bookings, passengers needing assistance, codeshare and interline cases, and anyone who asks for a person go to a human agent with a summary and the options already shown, so the passenger does not start again.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- Mobile app, Web chat, SMS, WhatsApp, Email, Phone and voice
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Interactions handled | Not pooled | 10,000 to 16 million | 2 | 1 organization, 1 vendor |
| Automation rate | Too few to pool | 97% | 1 | 1 vendor |
| Containment rate | Too few to pool | 45% | 1 | 1 vendor |
| Hours saved | Not pooled | 73,000 hours | 1 | 1 vendor |
| Satisfaction uplift | Too few to pool | 100% | 1 | 1 organization |
Value drivers: Customer experience, Lower cost to serve, Speed and cycle time, Employee productivity, Inclusion and access.
Indicative value
An airline carrying 20 million passengers a year
USD 160,000 to USD 3.6 million
Human handled disruption contact cost avoided per year
How this is calculated
Formula: passengers * disruptedShare * contactsPerDisrupted * containment * costPerContact. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Passengers per year passengers, passengers per year | 20,000,000 | 20,000,000 | The reference airline. |
| Share of passengers whose trip is cancelled, misconnected or significantly delayed disruptedShare, fraction of passengers | 0.02 | 0.05 | Editorial assumption; replace with your own irregular operations data. |
| Assisted contacts per disrupted passenger contactsPerDisrupted, contacts per disrupted passenger | 0.5 | 1 | Editorial assumption; many passengers accept the automatic reaccommodation, others contact more than once. |
| Share of disruption contacts the agent resolves containment, fraction of disruption contacts | 0.2 | 0.45 | This range sits at or below the 45% containment rate ASAPP reports for JetBlue's virtual agent across general digital support, because disruption contacts include complex itineraries that need a person. Air India's 97% figure is an automation rate for handled queries, a different metric, and is not used as a containment benchmark here. |
| Cost of a human handled contact costPerContact, USD per contact | 4 | 8 | Editorial assumption for a blended phone and messaging contact. Replace with your own fully loaded cost. |
What it leaves out: Gross avoided contact cost only. It leaves out the cost of running the AI and the integrations, the revenue kept by rebooking passengers instead of refunding them, the care and compensation costs (which the agent does not change) and the effect on loyalty and complaints.
Who already uses it?
6 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Delta Air Lines
United States · Travel and hospitality · 2025
Delta Concierge is an AI powered assistant inside the Delta app for SkyMiles Members. It authenticates the member, recognizes the nearest upcoming trip, explains what changed when a flight is disrupted and helps find alternative flights, cancels eligible flights and submits a refund request or issues an eCredit in real time, and summarizes bag status. It launched in beta to selected members from October 2025, was expanded in phases and became available to all SkyMiles Members in August 2026, with a handoff to Delta staff when it cannot help.
No outcome disclosed.
United Airlines
United States · Travel and hospitality · 2024
Customer service teams in United's network operations centre use generative AI to review flight data and write the text and email messages that explain why a flight is delayed or changed, including links to live radar maps during weather delays. When a flight is delayed or cancelled, United's self service tools automatically present personalized rebooking options, bag tracking and meal and hotel vouchers when eligible. Its AI powered ConnectionSaver tool identifies departing flights that can be held for connecting customers without delaying the on time arrival of those already on board; United says it has saved more than 3.3 million customer connections since launching in 2019. That tool works on the operation, not in conversations with passengers.
No outcome disclosed.
Pegasus Airlines
Türkiye · Travel and hospitality · 2025
Pegasus Airlines retrained FlyBot, the virtual assistant on its website, with Azure OpenAI and integrated it with internal systems, so customers can ask about flights, flight rules, baggage allowances and claims and reissue tickets in the same conversation. The airline reports that satisfaction with the virtual assistant doubled after the change.
- Satisfaction uplift: 100%
"“Since we integrated Azure AI Services into our FlyBot, customer satisfaction rates for our virtual assistant have doubled,” points out Bora."
Claimed by: organization
Air India
India · Travel and hospitality · 2023
Air India launched AI.g in May 2023, a virtual assistant on Azure OpenAI that is integrated with the reservation system and answers questions across 1,300 topic areas including bookings, flight status, baggage, check in, frequent flyer awards and lounge access, and escalates automatically to contact centre staff when it detects the need. The airline says it has kept contact centre call volume flat while its passenger count doubled.
- Automation rate: 97%, cumulative, of nearly 4 million queries
"To date, AI.g has successfully handled nearly 4 million customer queries, 97% of them with full automation."
Claimed by: vendor - Interactions handled: about 10,000, per day
"That's because AI.g is handling about 10,000 a day."
Claimed by: organization
Lufthansa Group
Germany · Travel and hospitality · 2020
During the pandemic, when passengers flooded call centres to change or cancel flights, Lufthansa Group replaced its in house chatbot with a conversational AI platform and built self service AI agents that manage rebookings, check alternative flights, give travel information and process refunds. The agents run on the airline websites and through SMS links that open a self service chat, with multilingual support and real time translation, and are used to absorb peaks such as strikes.
- Interactions handled: about 16 million, per year
"By leveraging AI-driven Self-Service Agents, the airline managed to significantly increase its interaction capacity, handling about 16 million conversations throughout the year with AI, with peak days seeing up to 375,000 interactions."
Claimed by: vendor
JetBlue
United States · Travel and hospitality · 2019
JetBlue moved its customer support to an AI platform from late 2019, opening messaging channels (Apple Messages for Business, Google Business Messaging, web and app chat, WhatsApp) with Spanish language support, a virtual agent that resolves routine requests and AI assistance for the crewmembers who handle the rest. In a January 2026 conference session published by the vendor, a JetBlue customer support leader described weather disruptions, when passengers ask for their options, and said the conversations crewmembers now handle (rebooking, refunds, alternatives weeks away) are multifaceted, which is why the airline has looked at AI that orchestrates several workflows. The ASAPP speaker in the same session warned against reading containment gains without checking whether customers still have the option to escalate.
- Containment rate: 45%, May 2023, virtual agent
"The integration of virtual agent experiences facilitated streamlined interactions and contributed to a remarkable 36% year-over-year growth in containment, with a 45% containment rate achieved in May 2023."
Claimed by: vendor - Hours saved: 73,000 hours, Q1 2023 only (one quarter, not annualized)
"In Q1 2023 alone, this AI-driven efficiency translated into significant savings of 73,000 workforce hours."
Claimed by: vendor
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Reaccommodation and waiver policies per disruption type, in a form the agent can apply
- Passenger rights rules per market (care, refund, compensation) with an owner and review date
- Real time flight status, delay reasons and the operational reaccommodation offer per passenger
- A catalog of disruption contact reasons with volumes from past irregular operations days
Systems to integrate
- Passenger service system and departure control (bookings, seats, standby lists)
- Ticketing, refunds, eCredits and vouchers (EMDs), and the payment service for fare differences
- Reaccommodation or disruption management engine
- Notification platform (app push, SMS, email, messaging)
- Baggage tracking
- Contact centre platform for handover with conversation context, and CRM for loyalty status
Complexity: High
Answering "where is my flight" is easy. Rebooking is not: the agent needs read and write access to the passenger service system, inventory, ticketing and EMD issuing, fare rules, the reaccommodation engine and the notification platform, plus the legal rules for care and compensation per market. It must also survive peak load on the worst day of the year.
- 1
Start with information, then add actions
First ship proactive, accurate delay and cancellation messages with the reason and the next step. Measure how many calls they prevent. Add actions (accept the new flight, refund, eCredit, voucher) one by one, each with its own tests and sign off.
- 2
Encode the rules, not just the knowledge
Put reaccommodation policy, waivers and passenger rights in deterministic rules or a flow that the agent calls, so the offer a passenger sees is the offer the policy allows. Let the language model explain the options, never invent them.
- 3
Rehearse the worst day
Load test the agent, the integrations and the handover queue at the volume of your largest irregular operations day, including the reservation system's rate limits. An agent that fails under peak load is worse than none.
- 4
Design the handover for disruption
Route complex itineraries, groups, unaccompanied minors, passengers needing assistance and codeshare or interline tickets to people, with a summary and the options already shown. Give the priority phone line to the cases the agent cannot solve.
- 5
Close the loop with operations
Feed contact reasons and failed rebookings back to the operations control centre and the reaccommodation team, so the automatic offer improves and the agent stops getting the same question.
- 6
Test before passengers do
Build test conversations per disruption type (weather, crew, technical, strike) and per market rule, including attempts to get a refund or compensation the rules do not allow, and run them on every change.
Guardrails
- The agent only presents options returned by the reaccommodation engine and fare rules, never options it generates itself
- Refunds, compensation and vouchers above set limits need a human approval
- Entitlement statements (care, refund, compensation) come only from approved, dated policy content, with a refusal and handover when the content does not cover the case
- Automatic handover for passengers needing assistance, groups, complaints and repeated failure
- Payment card data tokenized before it reaches the model, and personal data masked in logs
KPIs to instrument
- Share of disrupted passengers who rebook or accept an option without a human, per disruption type
- Handover rate and handover reasons on irregular operations days
- Repeat contacts within seven days on the same booking
- Accuracy of entitlement answers on a weekly reviewed sample
- Time from disruption notice to a confirmed new itinerary
- Complaints and chargebacks that mention the assistant
Human in the loop
Humans own the exceptions: complex itineraries, passengers with reduced mobility, groups, compensation disputes and complaints. The disruption desk watches live volumes and handover reasons during irregular operations, and a policy owner signs off every change to entitlement content and every new action before it goes live.
Common failure modes
- Wrong entitlement answers
- The agent promises a refund, compensation or discount the policy does not give, and the airline is held to it, as in the Air Canada tribunal case. Keep entitlement answers in approved content with an owner and review date, and hand over when unsure.
- Collapse under peak load
- The agent or the reservation integration times out on the busiest day, and passengers are pushed back to a full phone queue. Load test, cache flight status and queue writes.
- Options that are not real
- The agent shows flights that no longer have seats or that the fare rules do not allow, and rebooking fails at confirmation. Only show options from live inventory and confirm before telling the passenger they are rebooked.
- Containment that is really abandonment
- Passengers give up and go to the airport desk or book another airline, which looks like containment. Count repeat contacts and measure satisfaction on disruption days separately.
What are the risks and rules?
EU AI Act
Limited risk (transparency)
A customer facing assistant must tell people they are interacting with AI (Article 50). It is not a high risk use under Annex III: it applies the airline's reaccommodation rules and does not decide on access to an essential public service or on creditworthiness.
Rules that apply
Guidance
- Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). Passengers must be informed that they are interacting with an AI system unless this is obvious from the context.
- Air passenger rights (Your Europe) (European Union, Europe). Summary of the EU rules on reimbursement, rerouting, care and compensation for cancelled, delayed and overbooked flights; the agent's entitlement answers must match them for flights in scope.
- Flight delays and cancellations (UK Civil Aviation Authority, Europe). UK passenger rights guidance for delays and cancellations, the reference for entitlement content on UK flights.
Controls to put in place
- AI disclosure at the start of every conversation and in AI drafted disruption messages where required
- Entitlement and policy content versioned, owned and reviewed after every regulatory or policy change
- Immutable audit trail of every rebooking, refund, voucher and eCredit the agent issued
- Limits per action (refund value, voucher value, number of changes) with human approval above them
- Peak load and failover plan for irregular operations days
When it went wrong elsewhere
- Incident 639: Air Canada Chatbot Reportedly Provides Inaccurate Bereavement Fare Information, Leading to Customer Overpayment. A Canadian small claims tribunal held Air Canada responsible in 2024 for its chatbot's wrong statement about a bereavement fare refund (Moffatt v. Air Canada) and ordered it to pay damages. An airline cannot disclaim what its assistant tells passengers about refunds and entitlements.
Frequently asked questions
- What share of disruption contacts can an AI agent resolve?
- No airline on this page publishes a rate for disruption contacts alone, and it depends on whether the agent can act. Across general digital support, ASAPP reports a 45% containment rate for JetBlue's virtual agent in May 2023, and Microsoft reports that 97% of nearly 4 million queries to Air India's AI.g were handled with full automation. Disruption days are harder than average, because more itineraries are complex, so plan for a lower rate and a strong handover.
- Can an airline be held to what its chatbot says?
- Yes. In Moffatt v. Air Canada (2024) a Canadian tribunal held the airline responsible for its chatbot's wrong answer about a bereavement refund. Keep entitlement answers in approved, dated content and hand over when the content does not cover the case.
- Should the agent decide what a passenger is entitled to?
- No. Care, refunds and compensation should come from the airline's rules engine and approved policy content, applied the same way to every passenger. The agent explains the options and completes the chosen one; disputes and exceptions go to a person.
- Is this only for airlines?
- The public deployments on this page are airlines. The same pattern applies to rail and ferry operators and tour operators, wherever a disruption triggers mass rebooking and rule based entitlements.
How to cite this page
Blits.ai AI Use Case Library, "AI agent for flight disruption and rebooking", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/flight-disruption-and-rebooking-agent. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published