Data Orchestration: The Critical Foundation for Enterprise AI

Reading Time: 5 minutes

In short: Denodo delivers the AI data orchestration foundational layer: the infrastructure that determines what enterprise data every AI application and agent can access, trust, and act on, and it automates the delivery of that data. It dynamically connects, contextualizes, governs, and delivers data from across the enterprise, without requiring it all to be first copied into another repository, and without organizations having to build and manage a multitude of MCP servers. Zero-copy, no pipelines, no MCP sprawl.

AI Is Advancing Faster Than the Data Beneath It

AI models have become remarkably capable. But trusting an agent in production has proven to be very different than working in a controlled sandbox.

Camunda’s 2026 State of Agentic Orchestration and Automation report found that although 71% of organizations use AI agents, only 11% of agentic AI use cases reached production during the previous year. Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027.

The reason is not simply that enterprises need better models. They need better orchestration of the systems and information surrounding those models.

Anthropic’s 2026 State of AI Agents Report, based on insights from more than 500 technical leaders, reinforces the point. Integration with existing systems was the most frequently cited adoption challenge, identified by 46% of respondents, while 42% pointed to data access and quality.

The AI production gap is not a model problem. It is an orchestration problem, with data at the center of it.

AI Orchestration Is Only Half the Story

Most discussions of AI orchestration focus on coordinating the execution of agents and the tools they use. An AI orchestration platform might determine which agent performs a task, which model handles a request, when human approval is required, and the order in which these happen.

But every action depends on something more fundamental: the data the AI system uses to understand the situation and decide what to do.

Before an agent can resolve a customer issue, investigate fraud, or recommend a supply-chain action, it must determine:

• What data is most relevant and where it resides
• Whether that data is current and trustworthy
• What the data means in its business context
• How information from different systems relates
• Whether the requesting user or agent may access it

Coordinating these decisions is AI data orchestration.

AI orchestration coordinates what agents, models, and tools do. AI data orchestration provides the right enterprise context to do it.

Enterprise AI needs both. But data orchestration is foundational because agents cannot reliably reason or act on fragmented, ambiguous, stale, or improperly governed information.

The Evolution of Data Orchestration

Data orchestration is not new, but it has evolved to support a variety of new data initiatives. It began by coordinating batch jobs and ETL workflows, it expanded to manage cloud pipelines and distributed data platforms, and today it serves as a key component of modern data operations. 

AI initiatives introduce a different set of requirements. Agents and intelligent applications cannot rely solely on scheduled pipelines or precomputed datasets. They require live enterprise data, shared business meaning, and governance that is consistently enforced at runtime. The challenge is no longer just orchestrating data pipelines. It is orchestrating the enterprise context that AI depends on to make trustworthy decisions. What’s needed is the next step in the evolution: AI data orchestration.

The AI Data Orchestration Foundational Layer

Every AI application and agent needs a data access layer that determines what data it can see, how that data is interpreted, and how it may be used.

In many enterprises today, this is not really a consistent layer. Instead, they may have implemented a collection of application-specific pipelines, copied datasets, embedded business logic, independent vector stores, and duplicated security rules. If this is the case, each new AI initiative must reconstruct its own incomplete view of the enterprise.  This won’t scale beyond more than a few agents.

What is missing is an AI data orchestration foundational layer: shared infrastructure that dynamically coordinates how distributed enterprise data is accessed, combined, contextualized, governed, and delivered to AI.

How Denodo Provides the Foundation

The Denodo Platform provides this AI data orchestration foundational layer. It connects to all distributed data in the enterprise, determines how to access and combine it, gives it consistent business meaning, enforces policies at runtime, and delivers it to AI applications through open interfaces such as MCP.

The result is active context: live enterprise data combined with the right business context and governed by the right policies at runtime.

Denodo delivers this foundation through four essential capabilities:

• Universal Connectivity

AI initiatives rarely depend on data from only one platform. Even organizations with a strategic lakehouse still rely on operational databases, SaaS applications, multiple clouds, legacy systems, APIs, and documents.

Denodo creates a unified logical view across this environment, enabling AI to access relevant information wherever it resides. This reduces the need to create another integration pipeline for every source and use case.

• Zero-Copy Live Data Access

Different AI requests have different requirements for freshness, performance, and cost. Some need live operational data; others can use cached, indexed, or materialized information.

Denodo can federate queries, push processing into underlying platforms, use intelligent caching, and work with selectively materialized data. Rather than imposing one access method, it helps apply the right method to each request, without copying data by default.

• Unified Semantics

Technical access does not ensure the correct interpretation of all data. “Customer,” “revenue,” or “available inventory” may mean different things across business units and systems.

Denodo’s semantic layer presents data through consistent business entities, definitions, metrics, and relationships. This provides AI systems with the necessary context to find the appropriate information to use for each task, interpret it correctly, and explain conclusions using recognized business concepts.

• Centralized Governance

AI expands data access beyond employees and conventional applications to models, copilots, and autonomous agents, each having different policies governing their access and approved usage. Governance therefore must be applied at the time information is requested and used, depending on who or what is requesting it.

Denodo centrally enforces access controls, masking, privacy rules, lineage, and other policies across distributed sources. The data returned reflects the identity and permissions of the requesting user or agent, avoiding the need to recreate governance within every AI application or repository.

Foundational, but Not Another Orchestrator

Denodo does not replace existing data pipelines, workflow, or agentic AI orchestrators. It provides the trusted data foundation across all of them.

An AI orchestrator might determine that a specific series of agents should evaluate a given customer request. Denodo provides these agents with access to the right customer, contract, transaction, and policy information, with consistent meaning and appropriate controls, when that evaluation occurs.

These capabilities are complementary:

• Pipelines schedule predefined data-processing jobs.
• Workflow and agentic AI orchestrators coordinate agents, tools, people, and other process steps required to address a specific request or event.
• Denodo orchestrates the enterprise data and context on which those processes and agents depend.

 

The Foundation that Production AI Has Been Missing

Enterprises do not have a shortage of models, agents, or frameworks. What many lack is a shared foundation that gives those systems governed access to trusted enterprise data.

Without it, every initiative builds another partial view of the business. Integration complexity grows, governance becomes inconsistent, and AI pilots struggle to be trusted in production.

Denodo fills that gap. The Denodo Platform is the enterprise AI data layer — and, more specifically, the AI data orchestration foundational layer. It combines universal connectivity, zero-copy live data access, unified semantics, and centralized governance to deliver active context to any AI model, application, agent, or orchestration framework.

Before enterprises can orchestrate what AI does, they must orchestrate the data that tells AI what is true.

Want to learn more about how a data orchestration foundational layer can support your AI initiatives? Contact us for a demo or a free consultation.