The Operating System for Enterprise AI

Every conference session we attend, every vendor pitch we sit through, every other LinkedIn post delivers the same message: AI is coming for how you currently operate; get on board or get left behind.

To be fair, for many domains, this AI augmentation story makes perfect sense. Writers can use AI to draft content and overcome the dreaded blinking cursor in a blank document. Software developers can leverage copilots to automate boilerplate coding and accelerate development. Customer service teams can deploy chatbots to handle routine inquiries, freeing up human agents for the more complex cases. These are legitimate productivity multipliers.

But when it comes to data teams, success requires something more fundamental. The statistics tell a sobering story. According to various industry reports, between 60-85% of AI projects never make it to production due to poor data quality, inconsistent data pipelines, and lack of operational governance. Data security and privacy also become blockers the closer a project gets to production, with nearly 75% of technology professionals ranking it among their top 3 concerns last year, up from just 22% in the year prior.

While everyone seems to agree that "there is no AI strategy without a data strategy," all this points to a troubling trend: organizations investing heavily in AI while underinvesting in the orchestration layer that makes AI reliable and production-ready.

After months of conversations with data leaders across dozens of organizations implementing AI, we've discovered that success isn't necessarily about replacing your data infrastructure. Rather, it's about getting a tighter grip on the wheel. The organizations seeing real results from AI aren't the ones with the most advanced models; they're the ones coupling their models with robust orchestration layers that deliver the highest quality data.

The Complexity Doesn't Disappear

Many vendors claim AI will simplify data workflows, letting business users directly query data without technical expertise. It’s a great story: "Just ask the data a question, and AI will handle the rest!"

But it’s also a dangerous oversimplification. Consider what happens when a CRO asks, "Why did revenue drop last quarter?"

Behind the scenes, answering this question requires:

• Identifying relevant data sources across disparate systems
• Understanding business definitions of "revenue" (which often vary by department)
• Applying appropriate transformations and aggregations
• Ensuring data freshness and quality
• Securing sensitive financial information
• Providing auditable results

(And by the way, all this was true before AI. Before November 2022, the companies that could answer these types of questions at scale created robust data workflows built on top of orchestration.)

AI can help with each step, but it can't magically eliminate the complexity. It just pushes it deeper into the infrastructure:

• The AI needs high-quality embeddings of all relevant data
• Those embeddings need constant refreshing as data changes
• Prompts need careful engineering to ensure accurate interpretation
• RAG systems need framework-enforced guardrails to prevent hallucinations
• Results need validation and explainability mechanisms

AI doesn't make any of the requirements go away. Rather it amplifies them, especially for organizations that aspire to move beyond the very simple example of an exec asking a question to agentic applications where entire business processes are augmented or automated. The reliance on trustworthy, fresh data then becomes even more critical.

The organizations that will succeed are building robust systems to manage this complexity, not treating the prerequisites to AI as an afterthought.

The OS for Your AI

In the AI era, orchestration platforms become the lynchpin.

Just as no modern application runs directly on hardware without an operating system, no AI system can run directly on raw data. It needs an orchestration layer that abstracts away the complexity of the underlying components while providing consistent interfaces for development, deployment, and monitoring, as well as managing complex dependencies across workflows that if left unresolved would scuttle your SLAs for quality/freshness. In the near future, orchestration platforms won't just schedule tasks. They'll serve as the operating system upon which your entire AI architecture runs, providing:

• The contract layer defining how data moves between systems
• The observability plane monitoring quality and performance
• The governance framework enforcing security and compliance
• The coordination mechanism for human/AI collaboration
• The evaluation layer validating AI outputs for accuracy, coherence, and relevance, ensuring only high-quality responses are delivered

Back in February, the largest ever survey of data engineers was released in the State of Apache Airflow survey. It showed how the industry's leading orchestration tool was being used increasingly for AI/ML workloads.

This growing reliance on Airflow for AI isn’t surprising. As AI matures, Airflow is emerging as the go-to orchestration layer for bringing these workflows into production. What’s more striking is the impact of using the right tooling to support this. Among organizations using Astronomer's managed Airflow service, Astro, 55% are already leveraging Airflow for ML and AI workloads. That number jumps to 69% for customers who’ve been on Astro for two years or more—clear evidence that with the right platform, AI adoption not only increases, but accelerates over time.

Here are some examples to demonstrate how leading organizations are releasing meaningful AI-powered applications by building on orchestration at their core:

• One of the world’s largest online travel agencies uses Astro to orchestrate petabyte-scale data workflows powering machine learning, generative AI, and real-time search ranking. With Astro as a stable foundation, the team has launched conversational AI tools that help travelers plan trips from ideation to booking—processing billions of transactions and predictions daily.
• A global custom printing company uses Astro to automate GenAI-powered product onboarding across multiple languages and markets. By orchestrating everything from GPT-4 content generation to human-in-the-loop review and publishing, Astro helped the team replace a manual process with a scalable AI workflow—accelerating growth and cutting time-to-market for thousands of new products.
• A fast-moving financial services firm uses Astro to power AI-driven customer support, reducing manual ticket handling and accelerating response times. With clean, structured data reliably delivered to Snowflake via Astro, the team built and deployed an AI support app in just one week—enhancing efficiency while maintaining strict compliance and data security.

Where Data Teams Go From Here

As orchestration evolves into this "OS for AI," data teams will transform as well. The future isn't about doing the same things with better tools. It's about fundamentally reimagining data engineering around the new orchestration paradigm.

In the next couple of years (perhaps even sooner), we'll see data engineers transform from pipeline builders to data platform architects. They'll design comprehensive systems that adapt to changing requirements, establishing the rules and frameworks that AI agents operate within—much like platform engineers have done for software development.

The batch-oriented, scheduled approach dominating today's orchestration will give way to more event-driven systems that react to changes in data or business workflows to better suit the real-time needs of AI applications.

And the most valuable data professionals won't just build infrastructure, they'll own outcomes, working in much closer collaboration with their partners in the business. This isn't just a shift in skills; it's a shift in mindset from infrastructure maintenance to something more akin to data product management.

Building for the Future

According to Gartner, by the end of 2025 over 30% of generative AI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. The companies that avoid this fate will be those that understand orchestration's central role in AI success.

For organizations building AI strategy today, this perspective has profound implications:

• Invest in your orchestration layer alongside your AI layer, ideally even before. Without robust data foundations, your AI initiatives will collapse under their own weight—becoming part of the 60-85% that never make it to production.
• Focus on frameworks, not just features. The organizations that win won't necessarily have the fanciest models or algorithms—they'll have the most coherent systems for making them useful. Orchestration frameworks provide these systems.
• Build for composition, not customization. The complexity of AI demands modularity and reusability, not more bespoke solutions. Orchestration enables standardized components that can be composed into complex workflows.
• Prioritize observability from day one. When AI systems fail, they often fail in ways that are hard to diagnose. Comprehensive orchestration includes instrumentation that makes failure modes visible and addressable.

The companies that understand these shifts won't just deploy AI more successfully—they'll fundamentally reshape how their organizations create value from data.

As the models themselves become more standardized, it’s becoming clear that the future doesn't belong to the organizations with the “smartest” AI. Instead, it will belong to those with the best orchestrated AI.

At Astronomer, we are building towards that future. Our unified DataOps platform empowers teams to build, run, and observe mission-critical data workflows at enterprise scale. And with our SDKs, anyone can seamlessly integrate LLMs into these workflows and quickly turn the promise of AI into tangible business value. If you are interested in solving hard problems in this space, let’s talk.