The Skift Takeaway
Skift Data + AI Summit
Are You Operating AI or Just Deploying It?
VIPUL HINGNE, Interim Chief Technology Officer, Booking.com
Moderated by Seth Borko, Skift Research
THE ARGUMENT
AI has not eliminated the engineering bottleneck at Booking.com — it has moved it. Hingne said writing code used to be the constraint. Now that AI handles it in minutes, the new constraint is cross-team collaboration, as engineers spend more time coordinating than building. Booking’s competitive bet isn’t building more AI features than rivals — it’s building systems that scale to hundreds of millions of users. The reasoning is direct: failure to scale is itself friction, and reducing friction is Booking’s core mission. Hingne’s closing point landed as the session’s sharpest observation: the most powerful AI deployments aren’t consumer-facing features, but the infrastructure no traveler ever sees.
THE EVIDENCE
- “Challenges in cross-team collaboration is what we’ve seen” as the primary organizational side effect of deploying AI at scale, with bottlenecks shifting from coding to inter-team coordination.
- For well-bounded, deterministic tasks, AI agents can operate autonomously today; for higher-stakes decisions, Hingne said human oversight is still required. “I’m not yet ready to let it cancel my reservation.”
- “If we cannot scale, we are going to create friction in travel.” He framed scalability as the core engineering mandate, not a technical preference.
- AI tools don’t “have to necessarily be customer facing. How can you make everything that you do better using AI without even the customer noticing it?”
THE SO WHAT
If your AI roadmap is organized around customer-visible features, you are optimizing for the wrong signal. The internal deployment question of whether AI can make your systems faster, more scalable, and more reliable before it ever reaches the customer is where the competitive leverage sits. And if your engineering teams are not yet feeling friction from cross-team coordination, they will. That is the new bottleneck that follows AI adoption at scale, and it is an organizational problem, not a technical one.
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