The Current State of Airline Chatbots

Introduction

Chatbots have become a core component of airlines' digital strategy. In a landscape where passengers expect immediate, multichannel, and 24/7 assistance, conversational systems provide an efficient way to resolve inquiries, reduce operational workload, and enhance the overall travel experience.

However, the level of maturity across airlines varies significantly. Some carriers operate advanced chatbots capable of executing complete tasks, while others still rely on simple systems limited to FAQs or redirections.

To better understand how airline chatbots perform today, Kaiban conducted a functional study of 54 international airlines. The analysis was based on direct observation of each chatbot's behavior across key dimensions of automation, user experience, and personalization.

This article presents the complete data set — a comprehensive airline-by-airline comparison that reveals where the industry stands and where it needs to go.

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Executive Summary

We analyzed 54 international airlines to understand the current state of chatbot technology in aviation. Here's what we found:

• 91% have deployed conversational systems, but most remain rule-based (44%) or hybrid (20%)
• Only 20% use AI-powered natural language understanding
• 73% can only redirect users via links — they can't execute tasks directly in chat
• 82% operate without personalization — no account linking or contextual awareness
• Despite these limitations, 59% achieved "Good" or "Excellent" ratings, showing that well-designed basic systems still deliver value

The table below presents the complete airline-by-airline breakdown with detailed performance metrics across all seven evaluation dimensions.

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Complete Airline Chatbot Analysis

This table is the heart of our research — a comprehensive, airline-by-airline comparison of chatbot capabilities across the industry. Use it to benchmark your carrier against competitors or identify best-in-class implementations.

Note: "N/D" (Not Determined) indicates that the metric could not be evaluated, typically because the airline does not have an automated chatbot or only offers human-operated live chat services.

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Study Statistics

The study covered 54 airlines in total. The following summary highlights the main quantitative findings:

Key insights:

• Most chatbots remain rule-based or hybrid (64%), relying heavily on guided flows with limited execution capabilities.
• Only 20% have adopted AI-powered conversational understanding, indicating significant room for advancement in natural language processing.
• Account-linked personalization is rare (5%), representing a major missed opportunity for delivering tailored experiences.
• Despite these limitations, 59% of systems achieved Good or Excellent ratings, demonstrating that well-executed basic functionality can still provide value.

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Methodology: How We Conducted the Study

The evaluation was carried out manually through direct interaction with the chatbots available on each airline's official channels — including websites, mobile apps, and messaging platforms.

Each chatbot was assessed across seven dimensions designed to capture its real-world functional behavior and user experience:

1. Chatbot Type – Classified as:

• AI-Powered: Understands natural language and maintains conversational context.
• Rule-Based: Responds only to predefined commands or options.
• Hybrid: Combines free-text input with guided buttons.
• Live Chat: Managed entirely by human agents.
• No Chatbot: No conversational interface available.

2. Support Level – Defines the model of customer assistance:

• Self-Service: The bot resolves issues autonomously.
• Human Handoff: Transfers to a live agent by default or upon request.
• Mixed: The bot attempts to resolve first and escalates when necessary.
• Proactive: Initiates contact based on user behavior or specific events.

3. Capabilities – Describes the functional scope:

• Basic FAQ: Provides static information or links.
• Support Only: Allows submission of service requests.
• Booking & Reservations: Enables users to search or select flights.
• Post-Booking Services: Supports check-in, seat changes, or similar actions.
• Full Service: Integrates FAQ, booking, and post-booking directly in chat.

4. Execution Capability – Measures the level of task completion inside the chat:

• None: Static text only.
• Links Only: Redirects users externally to complete tasks.
• Dynamic Tools: Executes parts of a task (e.g., forms or searches) within chat.
• Full Execution: Completes the entire transaction inside the chatbot.

5. Performance (Overall Score) – Evaluates the overall quality of interaction:

• Excellent: Fast, accurate, intuitive; handles edge cases well.
• Good: Functional, with minor confusion.
• Average: Understands basic inputs but often incomplete.
• Below Average: Inconsistent or frequently unclear.
• Poor: Fails to interpret or execute most requests.

6. Data & Personalization – Indicates the level of contextual awareness:

• Anonymous: No data or personalization applied.
• Session-Based: Retains context within the same session.
• Account-Linked: Accesses user account data for personalized responses.

7. Response Time – Observed responsiveness:

• Instant (<1s), Fast (1–3s), Moderate (3–10s), Slow (10–30s), or Very Slow (>30s).

Study Limitations

Important: This assessment was completed on July 22, 2025 through manual evaluation of each chatbot. Results reflect observations at that specific moment and should not be considered definitive or exhaustive performance benchmarks.

The study has several important limitations to consider:

• Subjective Evaluation: All findings are based on human interpretation during direct interaction, without automated testing tools. Results may not capture all technical capabilities or reflect how the system performs under different conditions.
• Temporal Validity: Airlines continuously update their chatbot systems. The findings represent a snapshot from July 2025 and may not reflect current capabilities.
• Limited Testing Scope: Only publicly accessible chatbot interfaces were evaluated. Internal systems, agent-facing tools, premium customer channels, or authenticated user experiences were not included.

If your airline has made significant improvements since July 2025, please contact us to review and update your entry.

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Recommendations

Based on the findings, Kaiban proposes several priority actions to enhance chatbot performance and passenger experience across the airline industry:

1. Reduce response time to under five seconds and include progress indicators for transparency.
2. Ensure clarity and trust by keeping conversational messages consistent, contextual, and informative.
3. Integrate user feedback loops, enabling response ratings, comments, and chat transcript downloads.
4. Enable seamless escalation to human agents when the chatbot cannot provide a resolution.
5. Adopt an empathetic and contextual approach, avoiding early personal data requests and using natural greetings and closures.

These recommendations represent practical, cost-effective measures that can deliver immediate improvements in user satisfaction while laying the foundation for more intelligent, autonomous conversational agents in the future.

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Conclusion

The study confirms that 91% of analyzed airlines have implemented some form of chatbot system, yet functional maturity remains highly uneven. Guided, rule-based experiences dominate the landscape, revealing a significant opportunity for evolution toward higher levels of automation and personalization.

The next stage of this evolution requires transitioning from reactive chatbots to intelligent agents — systems capable of understanding context, executing end-to-end tasks, and continuously learning from operational data to improve over time.

Kaiban supports this transformation through a platform designed to build, manage, and monitor intelligent agents that seamlessly integrate with airlines' existing systems — delivering advanced capabilities without the need for costly migrations or infrastructure overhauls.

The future of airline conversational technology will not be defined by how many chatbots an airline deploys, but by how intelligently those agents collaborate, learn, and act — creating measurable operational value while delivering the seamless, personalized experiences that modern travelers expect.

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