The Inference
July 22, 2026
SEASON 1, EPISODE 3
In this episode of The Inference, Paul Curtis, CTO and Ecommerce Director at easyJet, joins host Michelle Hawley to discuss what it actually takes to move AI from proof of concept into production inside one of Europe's largest airlines.
Drawing on easyJet's experience running over 150 POCs, Curtis shares how the company has narrowed its focus to AI use cases that deliver measurable value, from empathy-aware customer service bots to hyper-personalized holiday content tailored down to the individual. The conversation covers how composable architecture and MCP adoption have become essential prerequisites for agentic AI, why total cost of ownership is the question most executives are still avoiding and how the build-vs.-buy calculus is shifting as SaaS products lose ground to custom AI solutions.
Episode Transcript
easyJet once had roughly 150 artificial intelligence proofs of concept running at the same time. Few produced meaningful business value.
That experience pushed the European airline toward a more disciplined AI strategy focused on practical use cases, modern architecture and clearly defined limits on autonomy, according to Paul Curtis, easyJet’s chief technology officer and ecommerce director.
Enterprise AI Needs Better Use Cases
easyJet initially explored a wide range of possible AI applications.
“At one point we had something like 150 POCs running at any one time, but so few of those then actually turned into something tangible with real business value,” Curtis said.
The airline has since concentrated on use cases where AI can improve customer experience or generate measurable commercial results.
Where to Use (and Not Use) AI
In customer service, newer AI systems can handle more complex, multi-intent requests than traditional chatbots. A traveler might complain about a delayed flight, an incorrect seat and a missing onboard product in the same conversation. AI can identify each issue, determine whether compensation may apply and adjust the response based on the customer’s sentiment.
easyJet is also using AI to identify when automation should stop. Signals like elevated stress, sensitive language or a direct request for assistance can trigger an early handoff to a human employee.
“If we determine that actually there's a high degree of stress, that we think actually this customer really needs to speak to a human, it doesn't matter how empathetic our bot is, they're going to want to speak to a human,” Curtis said.
Hyper-Personalizing Experiences
The other major opportunity is personalization. Instead of placing travelers into broad marketing segments, easyJet can tailor recommendations based on an individual’s previous behavior and recent activity. “You can almost have a personalization experience down to a unit of one person,” said Curtis.
That could mean emphasizing children’s activities and water parks to a family, while highlighting restaurants, upgraded rooms and spa services to a couple. AI also gives the airline faster feedback on which recommendations and content are most effective. And agentic AI, said Curtis, could extend that model further.