Orchestration: The Missing Link for Enterprise AI in a Hybrid World

ALPHARETTA, GA – March 19, 2026 – Enterprises worldwide are pouring investment into Artificial Intelligence, yet a vast majority are struggling to move beyond isolated pilot projects to achieve true, enterprise-wide production. New industry research suggests the problem isn't the AI itself, but a critical missing piece in the underlying IT infrastructure: service orchestration.

A comprehensive report released today by Stonebranch, a provider of automation solutions, reveals a significant gap between AI ambition and reality. The company's 2026 Global State of IT Automation Report, based on a survey of 402 senior IT professionals, found that while AI adoption is growing, only 21% of organizations have successfully scaled AI workflows across their enterprise. The primary culprits are integration complexity, a lack of governance, and persistent skills gaps.

The findings point to a market at an inflection point, where the ability to coordinate automated processes across increasingly fragmented, hybrid environments has become the defining factor for success.

“Organizations are now building automation as strategic infrastructure — a governed, scalable foundation that spans hybrid environments, operationalizes AI, and delivers automation-as-a-service to thousands of users across the enterprise,” said Giuseppe Damiani, CEO of Stonebranch, in the press release. “The companies that get orchestration right are not just running more efficient IT operations. They are compressing time-to-market, strengthening their compliance posture, and accelerating the business outcomes that matter most to their stakeholders.”

The Hybrid Reality and Fragmentation Challenge

The report confirms that hybrid IT is no longer a transitional phase but the permanent state of operations for modern businesses. A staggering 88% of organizations now run hybrid environments, blending on-premises data centers, private clouds, multiple public cloud providers, and containerized workloads. This complexity, however, has created a management nightmare.

This complex landscape is mirrored in the tools used to manage it. The study found that 89% of enterprises use multiple workload automation or service orchestration platforms, with three distinct tools being the most common setup. This tool sprawl creates data and process silos, increases operational overhead, and introduces security vulnerabilities. It’s a significant source of friction that slows down innovation and prevents a unified view of business processes.

In response, the market is entering a period of active rationalization. A remarkable 78% of organizations surveyed plan to add or replace an automation platform in the near future. This signals a clear move away from simply acquiring more point solutions and toward finding a unified "control plane"—a central orchestration platform capable of managing workflows and data across all underlying systems, regardless of where they reside. This central nervous system is seen as essential for taming the chaos of the modern IT estate.

AI's Last Mile Problem: From Pilot to Production

The struggle to scale AI is a direct consequence of this fragmentation. While AI and large language model (LLM) tasks are appearing in workflows, they often remain trapped in departmental silos or development sandboxes. The report's finding that only one in five companies has achieved enterprise-wide AI production is a stark indicator of this "last mile" problem.

Independent industry analysis corroborates these challenges. Experts note that for an AI model to deliver business value, it requires a constant, governed flow of data, access to diverse compute resources, and seamless integration into production applications. Without a robust orchestration layer, these data pipelines are brittle, resource allocation is inefficient, and deploying models into live business processes becomes a complex, manual, and error-prone endeavor.

The Stonebranch report identifies WLA/SOAP platforms as the second most common mechanism for embedding AI into enterprise workflows, just behind tools provided by the AI vendors themselves. This highlights the critical role these platforms play in operationalizing AI—transforming a standalone algorithm into a reliable, integrated, and automated business service. Successful implementations, from predictive maintenance in manufacturing to real-time fraud detection in finance, depend on orchestrating the entire lifecycle of the AI model, from data ingestion and training to deployment and monitoring, all within the context of the broader business workflow.

A Strategic Shift: From Cost-Cutting Tool to Enterprise Service

Perhaps the most significant trend identified is the evolution in why companies are modernizing their automation stacks. For years, the primary driver was cost reduction. Today, that has changed. The 2026 report reveals that 69% of organizations planning a platform change cite the need for more functionality or a more modern solution as the primary driver—a figure that has jumped 21% since 2025.

This "functionality" is not about incremental features. It represents a fundamental shift toward an "automation-as-a-service" (AaaS) operating model. The most sought-after capabilities—self-service portals, intuitive user interfaces, SaaS deployment models, and AI-assisted workflow creation—are all geared toward empowering a broader range of users.

The data shows that while 93% of organizations maintain a centralized automation team, these teams are evolving from gatekeepers to enablers. They are building and governing platforms that support a distributed user base, with 67% of companies now having more than 201 self-service automation users across development, data, cloud, and business teams. This trend aligns with the rise of Platform Engineering, where internal teams create standardized, self-service tools that allow developers and other stakeholders to deploy and manage their own automated workflows safely and efficiently.

By providing a governed, user-friendly platform, IT can democratize automation, accelerating innovation while maintaining control and compliance. This new paradigm treats automation not as a siloed IT task, but as a strategic enterprise-wide service that is foundational to digital transformation and competitive advantage in an increasingly complex technological world.