LinkedInยทFriday, 31 July 2026ยท27d ago
๐ง๐ฟ๐๐๐ ๐ถ๐ ๐ฎ ๐ฑ๐ถ๐ฎ๐น, ๐ป๐ผ๐ ๐ฎ ๐๐๐ถ๐๐ฐ๐ต. ๐๐ป๐ฑ ๐บ๐ผ๐๐ ๐๐ ๐ฟ๐ผ๐น๐น๐ผ๐๐๐ ๐ด๐ฒ๐ ๐๐ต๐ถ๐ ๐ฏ๐ฎ๐ฐ๐ธ๐๐ฎ๐ฟ๐ฑ๐. Two campsโฆ
Joris Vanherp
Co-founder @ Maya - Helping travel companies captivate travelers and sell more trips
๐ง๐ฟ๐๐๐ ๐ถ๐ ๐ฎ ๐ฑ๐ถ๐ฎ๐น, ๐ป๐ผ๐ ๐ฎ ๐๐๐ถ๐๐ฐ๐ต. ๐๐ป๐ฑ ๐บ๐ผ๐๐ ๐๐ ๐ฟ๐ผ๐น๐น๐ผ๐๐๐ ๐ด๐ฒ๐ ๐๐ต๐ถ๐ ๐ฏ๐ฎ๐ฐ๐ธ๐๐ฎ๐ฟ๐ฑ๐.
Two camps dominate travel right now, and both are wrong. One says deploy AI everywhere today. The other says AI can't be trusted with anything real. But when the stakes are a โฌ5,000 family holiday, nobody wants ideology. They want evidence.
A Sabre exec put it well in a recent Skift report: the mistake is thinking trust is a switch you flip. It's a maturity curve, and the progression matters.
You start with augmentation, where AI drafts, summarises, and suggests while humans decide. You move to supervised automation, where AI executes narrow actions with explicit approval gates. Only then do you earn managed autonomy, where AI acts end to end, but only where reliability is proven.
You don't earn autonomy by declaring it. You earn it with evidence.
The key design principle isn't "keep humans in the loop." It's design for reversibility. Every capability needs a human fallback that's automatic, fast, and boring. That means running both in parallel for longer than feels comfortable: let the AI handle the interaction, let humans keep authority for the judgment calls, then expand autonomy only when the data says you should.
That's the step most companies skip. They want the autonomy before they've built the fallback, which is exactly the wrong order.
At Maya, we see the same pattern with the teams we work with: the best results come when they treat trust like an operational system, not a feeling.
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