What challenges did Priceline face before AI implementation?

Legacy infrastructure was spread across three data centers and slowed deployments, A/B testing, and time to market. Priceline needed a scalable, reliable platform to support global operations and customer demand. The company wanted to curate more personalized travel experiences and customer-facing generative AI features.

What was the impact of the AI solution?

Reduced processing time for data pipeline reporting from two days to under one hour. Doubled deployment speed. Launched the Priceline VIP loyalty program with BigQuery. Improved customer recommendations with generative AI.

Source

GCPEvidence: Medium50/100

Priceline: bespoke travel concierge and faster data pipelines using Google Cloud

Use case typeData platform modernizationUpdated May 13, 2026

Priceline modernized its IT and data platform on Google Cloud to improve time to market and create new customer-facing travel features. The company uses Google Kubernetes Engine and BigQuery as part of its platform, and incorporates generative AI to create a bespoke travel concierge within its chatbot. Priceline also uses Google Cloud to support faster reporting, loyalty features, and improved recommendations for travelers.

Organization: Priceline
Industry: Consumer & Food
Location: United States
Published: May 2026

Reported outcomes

95.8%

data pipeline reporting timeTime & speed

+100%deployment speed

Strategic outcomes

Customer experience & trustCreated a bespoke travel conciergeCustomer experience & trustLaunched Priceline VIP on BigQuery

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Data pipeline reporting time: 95.8%

Google Cloud Customer StoryMay 13, 2026Customer storyInferred claimMedium evidence strength

"from two days to under one hour"

Normalized claim

Deployment speed: 100% increase

Google Cloud Customer StoryMay 13, 2026Customer storyExplicit claimMedium evidence strength

"more than doubled the organization’s speed of executing new deployments"

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Priceline
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Story

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Data platform modernization
2Developer productivity
3Customer personalization

Legacy infrastructure was spread across three data centers and slowed deployments, A/B testing, and time to market.
Priceline needed a scalable, reliable platform to support global operations and customer demand.
The company wanted to curate more personalized travel experiences and customer-facing generative AI features.

Modernized the platform with Google Kubernetes Engine and BigQuery to support faster feature development and data management.
Built a developer pipeline on Google Cloud to automate design, development, and deployment processes.
Incorporated generative AI to create a bespoke travel concierge within its chatbot.
Used BigQuery to power the Priceline VIP loyalty program and customer-facing personalization features.

Technologies

Google Kubernetes Engine BigQuery DataStax Astra DB Google Cloud Storage Insights

Reduced processing time for data pipeline reporting from two days to under one hour.
Doubled deployment speed.
Launched the Priceline VIP loyalty program with BigQuery.
Improved customer recommendations with generative AI.

Sources & evidence1

Evidence: Medium50/100Evidence strength