An OS for the enterprise: AI that orchestrates everything

Look under the hood of any enterprise today, and you'll find a complex patchwork of automation tools, digital workflows, cloud platforms, and now AI agents. While each promises value, these systems are piling up like mismatched Lego bricks. Instead of bringing clarity and efficiency, many have introduced greater complexity.

Most enterprises focus on quickly introducing AI agents for specific tasks such as support ticket automation and contract handling. While this approach delivers some quick wins, it leaves a lot of value on the table because it fails to address many of the root inefficiencies built into processes. Most inefficiencies in complex enterprise workflows occur between tasks—in transitions, handoffs, and manual checkpoints.

The rise of the enterprise OS

What’s missing is the connective tissue and intelligence to manage enterprise processes end-to-end. For example, in traditional IT, an operating system doesn't merely run programs—it manages them, allocates resources, and ensures system-wide stability. Similarly, companies need an enterprise OS layer designed explicitly to track, manage, evaluate, and optimize enterprise processes.

That's where agentic orchestration comes in.

Agentic orchestration acts as the enterprise OS. While the idea of orchestrating processes isn’t new, applying agentic AI enables autonomous decision-making and adaptive problem solving to bring more power to process orchestration. This OS layer integrates processes and the data, automation, and AI tools supporting them, providing observability, intelligent routing, exception handling, and built-in governance.

An enterprise OS isn't about centralization for its own sake: it provides distributed intelligence within a shared frame of reference. Simply layering AI onto existing workflows typically yields modest efficiency gains (around 3–5%) rather than transformative improvements. Achieving substantial productivity and value from AI requires redesigning workflows and bridging the gaps between them. For this reason, leaders are focusing 70% of enterprise AI investments on process improvements.

Agentic orchestration can expand AI’s potential from performing individual tasks to intelligently managing entire processes. When processes are explicitly mapped and defined, deploying the right automations and AI agents exactly where needed becomes simple and effective.

Orchestration is the new differentiation

The promise of agentic orchestration is both tactical and strategic. In the short-term, it addresses costly inefficiencies that plague complex processes: delayed handoffs, routing errors, duplicated efforts, and hidden exceptions. For example, machine learning has long been used in claims processing for data ingestion, extraction, and processing; the use of generative AI has also enabled summarizing and querying of unstructured data from documents.

Now, with agentic orchestration, entire workflows can be managed autonomously, end to end: getting proactive and actionable process intelligence, triggering robots and agents to complete tasks, invoking models, escalating exceptions to humans, and tracking progress. This reduces cycle time, improves consistency, and enables truly scalable, lights-out claims handling.

In the bigger picture, agentic orchestration unlocks a new architecture for enterprise operations. It serves as a foundational layer that enables effective scaling of AI, supporting modularity and reusability. AI agents become plug-and-play across multiple workflows.

The orchestration layer ensures observability and auditability for compliance, offers dynamic adaptation to real-time context changes, and it provides system-agnostic interoperability—integrating seamlessly with both legacy and modern platforms. Built-in policy enforcement and governance maintain control and accountability even as AI agents increase autonomy.

Enterprise performance is increasingly defined by how quickly and intelligently a business can execute. And this, ultimately, is the point: enterprise success won’t come from adding more isolated products; it’ll come from orchestrating them to create value.

Learn about UiPath Maestro™ for agentic orchestration.

Sources:

• Anders Humlum and Emilie Vestergaard, Large Language Models, Small Labor Market Effects, Becker Friedman Institute, April 2025.

• Boston Consulting Group, AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value, October 24, 2024.