Travel and hospitality has never had more technology ambition, or more structural barriers standing in the way. Airlines and hotels are investing aggressively in AI, personalization, and real-time operations. But thin margins, chronic labor shortages, legacy infrastructure, and fragmented data mean most companies can't execute at the pace the market demands.

The gap between ambition and execution is a workflow and data orchestration problem. Every strategic priority in this industry, from deploying AI agents, to modernizing legacy platforms, and personalizing the guest journey depends on the same hard requirement: reliable, governed, observable data pipelines connecting dozens of fragmented systems in real time.

This guide profiles the five data-driven investment priorities that will define travel and hospitality over the next three years:

1. Make every journey AI-native
2. Modernize the stack
3. Personalize traveler experiences
4. Implement dynamic, smarter pricing
5. Unlock operational efficiency and agility

For each, we outline the business objective, the challenges holding companies back, the priority use cases, and the capabilities required to succeed, showing how the Astro platform built on Apache Airflow® supports them.

Why Airflow and Astro?

• Apache Airflow has grown to become the industry's most widely used system for orchestrating data workflows, as well as being one of the world's most active open source projects.
• Astro, Astronomer's unified orchestration platform, elevates Airflow into an enterprise-grade control plane purpose-built for high-scale AI and data driven environments.

Travel companies want AI embedded in every customer interaction, operational decision, and revenue optimization cycle. They don’t want bolt-on experiments, but durable, production-grade infrastructure. The ambition is real: 90% of travel executives say their organizations already use generative AI in some capacity, yet only 2% have deployed agentic AI at scale.

The gap exists because the industry's data foundations aren't ready. Guest, operational, and revenue data is fragmented across dozens of disconnected systems with no centralized ownership, and only 12.5% of travel brands feel prepared to scale AI as a result.

The shift from advisory AI (suggestions a human acts on) to agentic AI (autonomous multi-step execution with branching, tool calls, and real-time decision-making) is where the industry is headed. Getting there requires orchestration that connects siloed systems, delivers clean data to models in real time, and governs every AI decision with full observability.

Priority use cases to tackle

• Agentic disruption management where multi-agent systems autonomously rebook passengers, reschedule crew, re-route baggage, and arrange accommodations in parallel.
• Agentic AI concierge and virtual travel agents that research, plan, book, and manage multi-modal travel end-to-end.
• Predictive maintenance for fleet and facilities ingesting 500+ GB per flight (Boeing 787) or 28 TB monthly from 3,500 rail sensors (Deutsche Bahn) to predict component failures before service impact.
• AI-driven continuous pricing and marketing campaigns replacing static fare classes with dynamic pricing engines, supported with targeted marketing promotions.

Astro in Action

Airflow is already used by some of the most demanding AI companies and agentic workloads on the planet:

• OpenAI has standardized on Airflow across its business with over 7,000 pipelines spanning research to operations and finance, all while providing a foundation for 10x growth. You can read more in our Airflow in Action blog post.
• GitHub relies on Airflow to process billions of developer events per day, orchestrating feedback loops and detecting usage patterns that are used to continuously improve Copilot. The Airflow in Action post has more detail.

The travel and hospitality industry has followed suit, adopting Airflow and Astro to support its AI use cases. For example, Astro underpins booking.com's AI trip planner. Huy Dao, Director of Data Machine Learning Platform, at the company said

A leading international travel provider for ages 50+ faced orchestration gaps and limited bandwidth during their Oracle-to-Snowflake migration. Azure Data Factory and Amazon’s MWAA Airflow service couldn't scale to support their AI/ML workloads ahead of peak season. By selecting Astro the company accelerated its AI/ML rollout by 8 months and eliminated $125K per day in outage risk. The solution unifies data workflows, removes infrastructure management overhead, and enables seamless dbt integration. The company projects $100K+ in savings from sunsetting legacy cloud infrastructure while maintaining velocity for AI-driven analytics and personalization initiatives.

Engineering leaders want a composable, cloud-native data architecture where operational, customer, and analytical data flows seamlessly across the enterprise. This modernized architecture replaces monolithic legacy systems that block every downstream investment. Today airlines still operate core reservation and crew management systems approaching 60 years of age, and 93% of hotel leaders name system integration their top technology challenge .

But modernization stalls when data is fragmented and migration risk is high. Only 3% of travel and hospitality brands have fully integrated customer data, and companies cannot afford downtime during migration because there is no maintenance window for a global operation running 24/7/365.

The path forward is not a big-bang replacement. It is a phased modernization with an orchestration layer that bridges legacy and cloud systems, synchronizes data flows during transition, and provides the stable foundation that AI, personalization, and dynamic pricing all depend on.

Astro in Action

Data teams in travel and hospitality adopt Astro to retire the legacy schedulers that block their ability to ship new data products and workflows. Moving from legacy orchestration systems such as AutoSys, Control-M, Informatica, or Apache Oozie to Astro unlocks strategic and operational gains:

• Cut costs by up to 75%. Organizations moving to Astro typically realize major savings through reduced infrastructure, licensing, and operational overhead, freeing budget for innovation.
• Unblock agility and scale with cloud-native orchestration. Astro gives teams the flexibility, resilience, and scalability needed to support fast-moving data and AI initiatives without the constraints of legacy tooling.
• Attract and retain top engineering talent. Airflow embodies code-first and open source philosophies. Data teams recruit top talent more easily, onboard faster, and avoid lock-in to proprietary technology.

No matter what workload or legacy orchestration tool your organization is using, Astronomer's Professional Services team can help. The company's experts can build an operational framework to smoothly and safely migrate your workloads to Astro.

Global hospitality brand modernizes enterprise workflows with Astro

The hotel group faced mounting pressure to modernize orchestration across critical systems including loyalty, HR, and finance. Legacy tools like Oozie and Tivoli lacked scalability, visibility, and resilience, slowing operations and exposing compliance risk in data transfers and financial reconciliation across 10K+ properties.

The company standardized on Astro as their enterprise orchestration platform, modernizing 2K+ legacy file transfers and migrating 1K+ workflows. Astro delivered automated scaling, CI/CD, RBAC, and real-time observability. The platform now executes 200K+ pipelines monthly, improves compliance controls, reduces MTTR, and de-risks financial and regulatory data flows. The company uploads loyalty program data 7x faster and replaces multiple legacy orchestration tools with lower overhead and enterprise-wide standardization.

Global travel leader unifies data and AI pipelines with 20% faster delivery

A global leader in travel reviews and experiences struggled to scale their homegrown orchestration tool to support 40 data engineers. The legacy system caused onboarding delays, data quality issues, and lacked support for AI/ML workloads. MWAA couldn't deliver key capabilities like dbt integration, backfilling, and lineage tracking.

The company adopted Astro + Observe to modernize orchestration and unify Airflow 3 + dbt workflows. The platform delivered CI/CD automation, backfilling, and observability for SLAs, cutting development time by 20% and enabling scalable data and AI pipelines across teams. Engineer onboarding accelerated 3x, and the company consolidated its legacy ETL tool and orchestrator onto one platform.

Figure 1: With the Astro platform, data teams work with a unified data stack to build, run, and observe all of their critical data pipelines across AI, app, and analytics workflows.

Every travel and hospitality company wants segment-of-one recognition: every traveler known across every channel, with offers tailored to individual behavior and context in real time.

The business case is proven. Companies that achieve this generate 40% more revenue than those that don't prioritize personalization. But delivering it requires solving a data unification problem most companies haven't cracked. Guest and traveler data is scattered across 4–6+ disconnected systems per company with conflicting records and no common identity, and only 36% of travel companies sustain personalization across all touchpoints

The consequence is measurable: cart abandonment rates as high as 98%, airlines losing out on repeat bookings due to poor customer experience, and consumers getting frustrated when personalization is missing. Closing this gap demands orchestration that unifies fragmented data sources into persistent traveler profiles, triggers personalized actions from real-time signals, and enforces data quality before insights ever reach the guest.

Uber runs 250K+ daily workflows on Airflow to power global operations

Uber operates one of the world's largest Airflow deployments, executing over 250,000 workflows daily across 4,000+ data pipelines to power payments, pricing, fraud detection, and marketplace optimization. The platform orchestrates data pipelines processing trillions of events, enabling real-time decision-making for millions of trips worldwide.

Airflow underpins Uber’s data stack to automate ETL, train ML models, and deliver analytics that personalize rider experiences and driver allocation. With Airflow, Uber reduces time to production while maintaining operational excellence at massive scale. Learn more from our Airflow in Action blog post.

Luxury hospitality and gaming company accelerates personalized marketing with Astro

The company needed to modernize their marketing technology platform to deliver personalized player offers, faster campaign launches, and improved customer experiences. Custom tools and self-hosted open source Airflow instances slowed innovation and consumed excessive time on infrastructure management instead of business logic.

The company adopted Astro to centralize data orchestration across their marketing technology ecosystem. Astro became the backbone of their modern data platform, powering ingestion, transformation, and curation for customer data and personalization pipelines. The team automated Dag creation through a custom metadata layer, introduced advanced monitoring and observability, and integrated with Snowflake, Databricks, and other systems to streamline player segmentation and offer delivery. Results include reducing marketing offer delivery from 3 weeks to under 1 hour and stabilizing 54K+ monthly tasks powering customer engagement and personalization at scale.

The objective is continuous, AI-driven pricing that captures maximum willingness-to-pay across every customer, channel, and moment, replacing the rigid fare buckets and manually managed rates that leave billions on the table. Airlines could unlock up to $45 billion in additional value over five years through modern retailing, and hotels switching to AI-powered revenue management see 7–20% increases in RevPAR. But legacy pricing infrastructure cannot support this shift. Airlines still price on 26 rigid booking classes designed in the 1990s, and 98% of hotel revenue managers report losing revenue to rate leakage.

Modern pricing demands sub-second response times, ingestion of alternative data signals like weather and events, and metasearch shopping ratios of 1:20,000 that legacy systems simply cannot sustain. Key use cases include:

• Agentic autonomous revenue optimization where AI agents monitor demand and automatically adjust rates, create bundles, and implement changes within configurable guardrails.
• NDC offer-and-order transformation enabling personalized, dynamically priced bundles across distribution channels.
• Demand forecasting with alternative data signals ingesting events, weather, social sentiment, and search volumes to uplift Average Daily Rates and reduce last-minute revenue leakage.

Capturing the value of these use cases requires orchestration that delivers real-time data to pricing engines, scales elastically under extreme computational load, and maintains the audit trails that emerging regulatory scrutiny of AI-driven pricing demands.

Hospitality group scales orchestration for pricing and hotel operations

One of Europe’s largest hotel, resort and vacation property groups needed to migrate from legacy scheduling tools to a scalable orchestration platform for pipelines powering critical pricing, marketing, and operational decisions. They required a partner to support platform-level deployment and rollout across 10+ data engineering teams in the EMEA region.

The company adopted Astro to centralize Airflow orchestration, delivering seamless Snowflake and GitLab integration, simplified local installation, and visibility into task execution. Astro's UI, observability, and control capabilities enable the company to roll out the platform across multiple engineering teams with confidence. The solution delivers real-time orchestration for pricing algorithms and operational metrics that drive revenue optimization and business decisions across the organization.

Ride Sharing: When Growth Depends on Real-Time Data

As the business grew, a leading ride-sharing service needed to scale real-time services including dynamic ride pricing, driver proximity matching, driver and rider profile data, and automated driver payouts. Managing this at scale meant migrating 4,000+ pipelines in under a year, something self-managed orchestration could no longer support without slowing teams down.

By standardizing on Astro, the company unified orchestration across mission-critical workflows, freed engineering time, and identified $2,800 per day in infrastructure cost savings during migration while retiring multiple legacy tools.

Operators want a data-driven operational model that deploys the workforce intelligently, automates routine processes, and prevents disruptions before they cascade. The urgency is structural: the global hospitality sector will be short 8.6 million workers by 2035 and flight disruptions cost the airline ecosystem over $60 billion annually. Hotels cannot hire their way out of a labor shortage that has left employment 10% below pre-pandemic levels. Airlines cannot absorb IROPS costs that consume close to 10 percent of total revenue while margins sit at around 3.5%

The systems that could solve these problems include predictive maintenance, AI crew and housekeeping scheduling, building automation with smart energy management. All depend on real-time data integration across IoT sensors, operational platforms, and external signals. Without reliable orchestration connecting these sources and triggering automated action, operational intelligence stays trapped in dashboards.

WeWork achieves 10x faster deployment with Astro

WeWork needed to modernize data infrastructure to support rapid business growth and enable self-service analytics. Legacy orchestration tools couldn't scale, creating bottlenecks in pipeline development.

Astro reduces deployment times from hours to minutes, achieves 10x faster release cycles and accelerates time to market for new analytics capabilities. The managed platform eliminates infrastructure overhead, allowing the data team to focus on business logic. Astro's observability, CI/CD integration, and RBAC enables governance at scale while maintaining agility, orchestrating critical workflows powering operational dashboards, financial reporting, and member experience optimization globally. You can get more detail from the Astronomer and WeWork case study

Expedia orchestrates 200+ Airflow clusters with multi-tenant platform

Expedia shifted from fragmented team-by-team Airflow deployments to a platform-engineered, multi-tenant model that now runs 200+ isolated clusters supporting 180+ engineering teams. The platform orchestrates 14,000+ unique pipelines executing approximately 1.5 million tasks monthly.

Fully automated CI/CD through GitHub Actions handles testing, vulnerability scanning, and deployment, reducing Dag deployment time from 15 minutes to under 5 minutes. The platform cut cluster setup effort by 50%, dropping from a full week to roughly two days. Teams adopt 20+ new pipelines monthly through templates while maintaining flexibility to run different Airflow versions side-by-side. You can learn more from our Airflow in Action blog post.

Each initiative in this guide shares the same requirements:

• Clean, timely, governed data
• Reliable, observable pipelines across systems and environments
• Security, compliance, and cost control built into execution

That is the role of orchestration. The travel and hospitality companies that win the next decade will treat orchestration as the control plane for AI, modernization, and traveler experience, and they will operationalize it with platforms like Astro.

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