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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.

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.

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

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

If your AI pilots keep stalling before production, this session argued the fix is not better models but a named owner accountable for each agent.

Acquiring a shiny company to boost your AI ambitions may seem appealing, but buying can sometimes set a company back.

What makes an offering stand out is proprietary data and operational depth that competitors can't copy.

Marriott is now in the third phase of its AI rollout, with conversational search the highest-profile launch coming this year. Its top data and AI exec says the harder work is rethinking the workflows underneath.

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.