Expedia Group’s top technology executive made one thing clear at a recent industry summit: when it comes to customer data, the company builds its own tools rather than buying them from someone else.
Shilpa Ranganathan, the chief product and technology officer, laid out the rule Wednesday at the Skift Data + AI Summit in New York. “Anything to do with customers’ data and control, [and] privacy, we want to keep that as close to us as possible, so that’s a build,” she said.
A Hard Line on Customer Data
Her comment cuts to the heart of a debate many tech companies face. The build-versus-buy decision is rarely simple. Buying a ready-made AI system can speed up a project, but it also means handing over some control to a vendor. For a company managing booking histories, payment details, and travel preferences, that trade-off can feel risky.
Sensitive data is at the center of the travel giant’s operations. It handles millions of transactions each year.
Trust relies on keeping that information secure.
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Ranganathan’s approach suggests the firm views its own engineering team as the safest guardian of customer records.
The Temptation of Shiny Acquisitions
Acquiring a startup that specializes in AI may seem like a shortcut. But buying can sometimes set a company back, she noted. Integration problems, cultural clashes, and unexpected costs often follow a deal. A shiny new technology might not fit the existing stack as neatly as its marketing claimed.
Some observers in the tech industry point out that building in-house is not always faster. It requires investment in talent, infrastructure, and time. But for data that is legally protected under regulations like the GDPR or the California Consumer Privacy Act, building can reduce compliance headaches later.
The decision also comes down to what Expedia considers core to its business. For them, customer trust and data control are not optional features—they are the foundation. That makes a build strategy more than just a technical choice. It’s a business principle.
How AI Fits Into the Picture
Ranganathan’s comments arrived at a moment when many travel companies are racing to add AI features. Chatbots, personalized recommendations, and dynamic pricing all rely on data. Using third-party AI models for those tasks means exposing user behavior to an external provider. That’s a risk Expedia seems unwilling to take.
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Of course, not every capability needs to be built from scratch. The executive suggested a spectrum: some tools are fine to buy or partner on—especially those that do not touch personal information. But the line is drawn sharply at customer data. “That’s a build,” she repeated.
Outside Perspectives on the Strategy
Not all companies agree with that approach. Some large tech firms have built entire platforms by acquiring AI startups and then integrating them deeply. The difference often comes down to the nature of the data involved. A search engine or a social network may handle less sensitive personal info than a travel booking system that knows your passport number and home address.
Analysts have also pointed out that building AI internally requires a specific kind of expertise. Data scientists, machine learning engineers, and privacy lawyers do not come cheap. But for organizations like Expedia that operate at scale, the upfront cost can be outweighed by long-term security and control.
The broader trend in the industry has been a mix of both approaches. Many corporations now use a hybrid model: they buy generic AI infrastructure but build custom layers on top for their own data. That middle ground can offer speed without sacrificing privacy. But Ranganathan’s firm stance suggests the company leans heavily toward the build side when the stakes are high.
What This Means for the Travel Industry
Expedia’s decision could influence how other travel companies think about AI investments. If a major player signals that customer data is too important to outsource, competitors may follow suit. The travel sector has been slow to adopt large-scale AI in part because of trust concerns. A public commitment to building in-house might reassure customers that their information is not being shared with unknown third parties.
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At the same time, the strategy carries its own challenges.
Building takes longer.
It requires sustained spending on R&D. And if the in-house team falls behind the pace of external innovation, the company could lose a competitive edge. That is the classic tension that every product executive has to manage.
Ranganathan’s rule offers one answer: protect the data first, and figure out the speed later. Whether that pays off will depend on how well its engineers can keep up with the market’s demand for smarter travel tools.