Media and entertainment companies face a pivotal investment cycle. As streaming growth matures, ad-supported models surge, and generative AI moves from experiment to production, competitive advantage hinges on how well organizations orchestrate data, AI, and content operations at scale.

This guide profiles the five data-driven investment initiatives that will define the industry through the end of the decade:

1. AI/ML Productionization
2. Data Platform Modernization
3. Audience Intelligence for Retention
4. First-Party Data Monetization
5. Content Supply Chain Automation

Each initiative shares a critical dependency: reliable, scalable data pipelines and workflow orchestration. When these foundations are fragile, even the most promising initiatives stall at proof of concept. That’s why data teams at leading media and entertainment companies are turning to Apache Airflow® and Astro.

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.

Media and entertainment companies want to operationalize AI across content creation, operations, and audience engagement — but most remain stuck in experimentation. The prize is significant: McKinsey estimates AI could redistribute up to $60 billion of annual industry revenue within five years of mass adoption. The shift from pilot to production is accelerating — Deloitte projects that 25% of enterprises using generative AI have deployed AI agents in production, scaling to 50% by 2027. That trajectory demands production-grade orchestration, not ad hoc scripts.

Target Use Cases

• AI-generated metadata tagging and content classification: Automated enrichment of content libraries with scene-level tags, mood descriptors, and contextual attributes for search, ad placement, and recommendation targeting.
• Generative AI-driven localization at production scale: Dubbing and subtitling pipelines preserving emotional tone and lip-sync accuracy, reducing turnaround from months to weeks.
• ML-powered content greenlight and performance prediction: Models trained on viewership, demographics, competitive scheduling, and social sentiment to forecast performance before production investment.
• Automated compliance and brand-safety screening: AI scanning content frame-by-frame to flag rights conflicts, regulatory issues, and brand-safety risks before distribution.
• Multi-agent content operations with human-in-the-loop approval: Agentic pipelines where specialized agents assess assets, generate metadata, and prepare marketing materials, with human gates at editorial and legal decision points.

Why This Is Hard Today

When AI initiatives lack production-grade data infrastructure, a significant share of generative and agentic AI projects stall after proof of concept, driven by poor data quality and escalating costs. Studios report that AI tools trained on incomplete metadata produce unreliable outputs: dubbing systems that mangle context, recommendation models surfacing irrelevant content, classification systems missing rights restrictions.

The root cause is fragmented pipelines that cannot deliver clean, timely data to models. Without orchestration coordinating ingestion, transformation, training, validation, and deployment as a single workflow, teams default to manual handoffs that break under load. Engineering cycles burn on debugging rather than improving models, and leadership loses confidence in AI as a strategic lever.

Taking AI to Production

Airflow and 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, operations, and finance, all while providing a foundation for 10x growth. Read more.
• GitHub relies on Airflow to process billions of developer events per day, orchestrating the feedback loops used to continuously improve Copilot. Read more.

The media and entertainment industry is following the same path:

Investigative Journalism at the Financial Times. The Storyfinding team uses Airflow to orchestrate AI-powered pipelines that turn messy public datasets including PDFs, tables, unstructured text, into structured, queryable data for its 700+ journalists. Airflow coordinates ML-based entity extraction, RAG-driven document analysis, and automated alerting, powering investigations that uncover hidden financial relationships and government spending patterns at scale. Read more.

Contextual Ad Tech Innovator Scales Data Products to Power AI. A leading contextual advertising company was managing workflows and SLAs across four fragmented systems, slowing data product rollout and consuming senior engineering time. By adopting Astro with Astro Observe, the team unified orchestration and observability, retiring homegrown tooling and compressing new data product rollout from days to 10 minutes, freeing engineering capacity to scale agentic AI and MLOps use cases.

From AI to data monetization, every initiative in this guide depends on a capability most media and entertainment companies have not yet built: a unified data platform that breaks down silos between content, audience, advertising, and operational data.

McKinsey’s 2025 global survey found 75% of respondents expect their companies to build data-, analytics-, and AI-driven businesses within five years, the highest of any industry sector. Deloitte’s M&E outlook reinforces the stakes: studios and platforms need unified data and AI capabilities to compete, and most must modernize existing infrastructure first.

Target Use Cases

• Unified content-audience data lakehouse: Single platform combining content metadata, viewing telemetry, subscriber profiles, and ad performance in open formats (Iceberg, Delta Lake) for cross-domain queries.
• Cloud-native infrastructure for faster content and ad product launches: Replacing on-premises systems with modern cloud platforms that let teams ship new audience segments, analytics products, and AI features in days rather than quarters.
• Real-time content and streaming performance intelligence: Pipelines correlating CDN telemetry, viewer engagement, and content performance data to trigger automated quality remediation and enable programming decisions within minutes rather than overnight batches.

Why This Is Hard Today

Most major media and entertainment companies operate dozens of disconnected data systems: separate warehouses for content metadata, audience analytics, ad operations, and financial reporting, each with its own cadence, schemas, and access patterns. This fragmentation undermines every strategic initiative the business is investing in:

• Recommendation models without content metadata produce shallow suggestions.
• Ad teams with stale audience segments lose deals.
• Programming teams making decisions on overnight batches miss real-time signals.

When pipelines are fragile, having been built on ad hoc scripts, cron jobs, and undocumented dependencies, a single upstream schema change cascades failures across the analytics stack. Teams spend cycles firefighting rather than building, and leadership loses trust in data that arrives late, incomplete, or inconsistent.

Modernizing Your Platform

Astro in Action

Foursquare Unifies 9,000+ Data Assets. Foursquare was orchestrating billions of daily geospatial records across fragmented systems including self-hosted Apache Airflow, Luigi, and homegrown tooling, with no centralized visibility into data assets, dependencies, or access. By standardizing on Astro, the company unified orchestration under a single control plane, achieving 5x faster pipeline development, a 90% reduction in data discovery time, and centralized governance across 9,300+ data assets. Read the case study.

Digital Advertising Platform Scales to 10B+ Daily Rows. A US-based digital advertising company managing 40+ web properties needed to eliminate tool sprawl and tighten SLA governance across mission-critical pipelines. By replacing self-managed Airflow and point solutions with Astro and Astro Observe, the team unified orchestration and observability on a single platform, processing 10 billion+ rows daily, reclaiming engineering hours previously lost to fragmented tooling, and enforcing consistent governance across all data workflows.

Subscriber retention is now the most important financial lever for streaming platforms, publishers, and ad-supported media. Monthly streaming churn has climbed to 5.5% across major platforms, with 23% of subscribers classified as serial churners cycling through three or more services within two years. Yet only 50% of customer journeys are personalized end-to-end (EY research), a gap representing billions in unrealized lifetime value. Closing it requires ML-driven personalization and real-time audience intelligence at scale.

Media and entertainment companies are targeting use cases across the full subscriber lifecycle: real-time churn scoring that triggers retention offers within hours of engagement decay, dynamic content personalization tuned to device, time of day, and viewing history, and unified identity resolution across web, app, and CTV. Increasingly, agentic AI is entering the mix with autonomous re-engagement agents that select win-back tactics, execute campaigns, and self-optimize without manual intervention.

Why This Is Hard Today

When audience data is siloed across streaming platforms, CRM, ad servers, and web analytics, personalization degrades. Customers regularly report negative personalization experiences that feel intrusive or irrelevant, increasing the risk of disengagement.

The failure is a data orchestration problem: signals arrive from dozens of sources at different frequencies, in inconsistent formats. When pipelines break, recommendation models train on stale data, churn predictions miss behavioral shifts, and campaigns fire days late. For a 50-million-subscriber platform at 5.5% monthly churn, even one percentage point of improved retention translates to hundreds of millions in preserved annual revenue.

Reducing Churn

Astro in Action

Sports Programming Network Scales Audience Analytics and Cuts Costs. The company relied on self-managed Airflow for mission-critical audience analytics powering regulatory reporting and executive decision-making, but its deployment lacked autoscaling, straining resources and driving up costs. By migrating to Astro, the team achieved $300k in annual infrastructure savings, a 305% increase in pipeline throughput per worker, and stabilized the data pipelines that leadership and compliance teams depend on daily.

International Sporting Body Delivers Real-Time Audience Data Across Global Events. The organization needed flawless data delivery across a complex stack to support events running around the clock worldwide. By adopting Astro with Astro Observe and Cosmos for dbt orchestration, the team cut troubleshooting time by 25%, gained proactive alerting for mission-critical audience data pipelines, and eliminated outsourced infrastructure management, ensuring reliable, timely audience intelligence reaches every event and digital touchpoint.

The convergence of ad-supported streaming growth, third-party signal deprecation, and tightening privacy regulation has made first-party data the most consequential commercial investment across media. 71% of net new SVOD subscribers over recent quarters came from ad-supported plans, with 46% of all U.S. streaming subscribers now on ad tiers. Yet 74% of publishers remain mostly reliant on cookies and third-party identifiers, and 70% lack adequate identity data management for the post-signal-loss era.

Why This Is Hard Today

Most media advertising infrastructure was built around third-party signals that are disappearing. With the majority of the open web effectively cookieless and only a minority of publisher visitors authenticated, legacy-targeted campaigns see declining CPMs and advertiser attrition.

When first-party data pipelines are fragmented, clean room partnerships fail, programmatic ad rates soften, and streaming platforms lose ad dollars to social competitors with superior targeting precision. Every hour of late or incomplete audience data represents lost yield on inventory that cannot be resold.

Unlocking the New Advertising Currency

Astro in Action

AI-Driven Ad Platform Scales ML Pipelines to 9PB Daily. An ML-powered advertising platform turning first-party data into revenue through targeted ad campaigns was running dual Airflow environments that consumed engineering time and created performance risks across core data pipelines. By consolidating on Astro, the team unified orchestration of the full ML lifecycle. As a result, they are achieving a 44% reduction in Dag processing times, 35% lower environment costs, and stable orchestration of 9 petabytes of daily data powering its ad targeting models.

Global content spending reached approximately $248 billion in 2025 while growth has nearly flattened, forcing studios and platforms to extract more value from every production dollar. McKinsey estimates AI could influence 20% of original content spend within five years, with early adopters reporting mid-single-digit productivity gains in pre- and post-production. IDC’s first-ever MarketScape for integrated media cloud solutions confirms that cloud-native integration across production, distribution, and monetization is now a competitive necessity.

Target Use Cases

• AI-driven localization across 50+ languages: End-to-end workflows generating AI dubs with lip-sync accuracy, producing culturally adapted subtitles, and routing through QC before multi-territory distribution.
• AI-powered metadata enrichment and rights validation at catalog scale: Automated scene-level tagging for search, recommendation, and ad placement, combined with pipelines cross-referencing licensing agreements and windowing rules to flag distribution conflicts before scheduling commitments.
• Autonomous content preparation agents: Agentic AI ingesting finished assets, verifying rights, triggering transcoding, and routing packages for multi-platform distribution with human approval at editorial and legal checkpoints.

Why This Is Hard Today

Content supply chains in most studios remain stitched together with manual handoffs, email approvals, and systems designed for linear broadcast rather than global multi-platform distribution. When rights data lives in spreadsheets, localization runs through project management tools, and metadata is entered manually at each stage, content sits idle for weeks awaiting clearance, localization bottlenecks delay international launches, and titles surface incorrectly in search and recommendation. Every day of delay erodes the monetization window.

Without automated pipeline management, supply chain costs scale linearly with catalog growth rather than generating operational leverage.

Automating the Supply Chain

Astro in Action

Create Music Group Orchestrates Content Operations Across Global Platforms. Create Music Group orchestrates 600+ pipelines on Astro to connect streaming platform data from Spotify, YouTube, Apple Music, and Amazon into a unified content operations layer powering real-time artist analytics, revenue forecasting, and catalog acquisition decisions. By migrating from legacy scheduling to Astro, the team achieved 60% faster pipeline development and cut infrastructure management time by 50%, accelerating how quickly content performance data reaches the artists and labels who act on it. Read the case study.

Orchestration as the Control Plane for Media & Entertainment’s Next Decade

From productionizing AI and retaining subscribers, to monetizing first-party data, automating content supply chains, and unifying data platforms, each initiative in this guide shares the same foundational requirements:

• Clean, timely, governed data
• Reliable, observable pipelines across systems and environments
• Scalability and cost efficiency that adapts to unpredictable content, audience, and advertising workloads

That is the role of orchestration. The media and entertainment companies that win the next decade will treat orchestration as the control plane for AI, content operations, and audience engagement, and they will operationalize it with platforms like Astro.

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