Travel Technology

The hospitality companies scaling AI fast aren't running better pilots. They've stopped thinking in pilots at all.

Many of the pressing issues in AI implementation are organizational, not technological.

If your AI investment is being justified internally as labor savings, this session argued you're underselling the revenue case and overselling what's possible.

If your AI roadmap is organized around customer-visible features, you are optimizing for the wrong signal.

If your organization is still waiting for agents to mature before committing to a production deployment, the waiting period is already costing competitive ground.

If you're setting your AI roadmap around build versus partner or buy, Ranganathan's framework suggests a major issue is whether the capability touches customer data and trust.

Priceline's transparency play — show users their data, let them correct it — is a direct answer to the trust problem that has slowed AI adoption in consumer travel.

If you are pitching travel AI to investors, lead with cost savings you can put on the income statement, not revenue lift you can only promise — measurability is what closes the deal.

AI has gotten remarkably good at helping travelers decide where to go. The harder problem is getting them to book. Travelport's Fahim Khan argued that as more travelers start with LLMs, pricing and transaction capabilities will need to move higher up the funnel.

The hard part of scaling AI isn't the technology, it's the people.