Passenger Experience Under Pressure: An Operational Playbook for Airlines

Pressure in the contact centre is a symptom. The real story is happening in the passenger's journey.

When airlines talk about pressure on customer support, the conversation almost always defaults to queue load. But the load doesn’t appear out of nowhere; it’s generated by a passenger somewhere in their journey, living through a moment that will quietly decide whether they ever fly with that airline again.

When a passenger has a bad experience, calls three times about the same rebooking, and never books with the airline again, none of that shows up as a specific line on a P&L. It gets absorbed into “customer relations costs” and disappears from view, along with the revenue it took with it.

This session makes the case that passenger experience is a financial performance driver. Treating it purely as an operational or cost question leaves real money on the table, and AI changes what’s possible only when it’s applied to the right part of the problem.

Two expert perspectives

• Jerry Angrave opens with a reality check on where pressure actually originates. Drawing on his own research into passenger disruption, he unpacks why a single bad experience can be enough to lose a flyer for good, and why empathy is what determines whether passengers feel taken care of.
• Ellina Bronnikova brings the operational and AI delivery lens from running outsourced airline support at scale. She breaks down why AI applied to a broken process simply scales the mess faster, and why the next generation of AI agents should work like a bridge to a human, not a wall built to contain a call.

What we discuss

• Why load is a symptom, not the problem: how unresolved passenger experience issues convert into failure demand, repeat contacts, and lost lifetime bookings that never show up as a clean metric.
• The real source of contact centre pressure: the gap between brand promise and operational reality, and why ambiguity, inconsistency, and a lack of empathy generate far more volume than the original disruption itself.
• From walls to bridges: what separates AI built to contain volume from AI built to genuinely help, including the role of “hand of integrity” so passengers never have to start over with a human agent.
• Rolling out AI without breaking live operations: a phased approach—read-only, shadow mode, narrow intent—that protects passenger trust while testing what actually works.
• Keeping brand tone consistent at scale: why tone has to be engineered through living playbooks, AI-powered QA, and language-specific training rather than left to chance as outsourced teams and volumes grow.
• Translating CX into language leadership understands: reframing metrics like first contact resolution as signals of trust, and connecting better passenger experience to measurable brand strength, loyalty, and revenue.

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