From AI Proofs of Concept to an Enterprise AI Operating Layer
Enterprises are racing to deploy AI, but most still struggle to turn successful pilots into scalable, governed systems. IBM data shows only a minority of initiatives deliver expected ROI or achieve enterprise-wide deployment, even as AI infrastructure spending rises sharply across compute, data centers, and power. This disconnect is opening a new market for enterprise AI orchestration: platforms that centralize agent management, data integration, and operational governance into a coherent operating layer. Rather than adding more isolated models, organizations are rethinking how work is designed, how data flows, and how AI agents are supervised in production. IBM, Rescale, Xurrent, and Corvic AI are each attacking different parts of this problem—business operations, engineering simulation, IT service management, and data engineering—but all are converging on a similar goal: turning AI agents into dependable, auditable digital co-workers that can be scaled across the enterprise with consistent control.
IBM’s Agentic Control Plane for Operating AI at Scale
At its Think 2026 conference in Boston, IBM introduced a portfolio aimed squarely at the scaling gap between AI pilots and production. The company is framing an AI operating layer built around four pillars: agents, data, automation, and hybrid cloud. A centerpiece is the next-generation watsonx Orchestrate, positioned as an agentic control plane that coordinates AI agents, connects them to real-time data, and ties their actions into intelligent operations and sovereignty tooling. IBM argues that enterprises pulling ahead are not just deploying more models, but redesigning how their businesses operate with AI-driven workflows. With research from firms such as Morgan Stanley, Goldman Sachs, and Deloitte all pointing to rapid spending and lagging proof of value, IBM is targeting the control and governance side of AI adoption. The ambition is to give organizations a single orchestration fabric that makes production AI agents auditable, secure, and aligned with business outcomes.
Rescale’s Agentic Digital Engineering for AI-First Product Development
Rescale is bringing enterprise AI orchestration into the heart of R&D with its new agentic digital engineering capabilities. Engineering teams in sectors such as aerospace, automotive, energy, life sciences, defense, semiconductor, and manufacturing often juggle siloed tools for simulation, data, and AI. Rescale’s platform tackles this fragmentation by unifying those capabilities and embedding simulation-native AI agents across the product development lifecycle. These agents automate tasks like input validation, troubleshooting, report generation, and hardware selection, while keeping engineers firmly in the loop via an agent library, deployment framework, and workflow builder. Organizations report fewer simulation errors and less wasted compute as manual setup work is offloaded to agents. Rescale also extends its AI physics operating system into an end-to-end environment for turning simulation data into production-ready surrogate models. This positions the platform as an AI operating layer for digital engineering, where production AI agents continuously optimize design and simulation workflows.
Xurrent’s Autonomous Agents and Open MCP Server for IT Operations
Xurrent is extending its AI-powered service and operations management platform with autonomous agents and an open Model Context Protocol (MCP) server, targeting AI agent deployment in corporate IT and managed service providers. Unlike traditional assistant-style tools, these agents act as digital team members that can triage requests, perform knowledge work, and close tickets end-to-end, with humans setting guardrails and approving sensitive actions when necessary. Xurrent’s architecture was built for agentic AI from the start, with a Shared Policy and Data Layer that unifies governance, visibility, and security across workflows. Every agent—whether created by Xurrent or by customers—operates against the same service catalog, data model, and policies, ensuring consistent control and a full audit trail. The open MCP server connects Xurrent to external AI models from any provider, making the platform an orchestration hub for production AI agents in IT operations, rather than a point solution bolted onto legacy stacks.
Corvic AI’s Agentic Data Engine as the Logic Layer for Production AI
Corvic AI focuses on a different but critical layer of enterprise AI orchestration: turning fragmented operational data into reusable intelligence for production AI agents. With Corvic V3, now in general availability, the company is rolling out its Intelligence Composition Platform, described as a logic layer connecting enterprise data to production AI. Many industrial, manufacturing, field services, and life sciences organizations generate vast amounts of data buried in P&IDs, PDFs, sensor logs, invoices, and equipment schematics. Corvic’s agentic data engineering engine ingests this multimodal data and produces structured outputs without forcing teams into rigid schemas or brittle pipelines. V3 adds advances in multimodal retrieval, adaptive orchestration, workflow composition, and production reliability, allowing enterprises to move from experimentation to measurable outcomes without months of infrastructure work. By composing intelligence directly across existing data, Corvic positions itself as the orchestration layer that bridges fractured evidence and the production AI agents that depend on it.