From AI Experiments to an Enterprise Operating Layer
Enterprises are spending heavily on AI, but most still struggle to scale beyond pilots. IBM’s latest CEO study highlights that only a minority of initiatives reach expected ROI or enterprise-wide deployment, even as capital expenditure on AI infrastructure soars. The core problem is no longer just model performance; it is how to run AI reliably as part of day‑to‑day business. Vendors are responding with enterprise AI infrastructure platforms that act as an operating layer above models and hardware. These platforms integrate AI agent orchestration, real-time data pipelines, data governance platforms, and AI operations management to turn scattered proof‑of‑concepts into production AI deployment. Instead of isolated tools, enterprises are looking for unified stacks that combine orchestration, security, and compliance with the flexibility to support multiple models and clouds. The emerging consensus: scaling AI is fundamentally an architecture and operations challenge, not a lab experiment.
IBM Targets the AI Control Plane with watsonx and Sovereignty Tools
At its Think 2026 conference in Boston, IBM framed the next phase of AI as an operating-model transformation. The company introduced capabilities across four pillars it considers essential to enterprise AI infrastructure at scale: agents, data, automation, and hybrid cloud. A centerpiece is the next-generation watsonx Orchestrate, positioned as an AI agent orchestration and control plane designed to coordinate multiple agents, workflows, and applications in production. Alongside this, IBM highlighted new real-time data services, intelligent operations features, and sovereignty tools intended to ensure governed, compliant AI operations on hybrid environments. The aim is to close the widening gap between AI spending and measurable payoff by standardizing how enterprises manage multi-agent coordination, data access, and operational policies. IBM’s message is clear: scaling AI means building a persistent, policy-aware layer that governs how agents interact with business processes and sensitive data.
Broadcom’s VMware Cloud Foundation 9.1: Private Cloud for Production AI
While IBM focuses on orchestration and governance, Broadcom is attacking the infrastructure layer with VMware Cloud Foundation (VCF) 9.1. Positioned as a secure, cost-effective platform for production AI workloads, VCF 9.1 offers an AI- and Kubernetes-native private cloud that supports mixed compute across AMD, Intel, and Nvidia. This lets enterprises run both inference and agentic AI applications while retaining hardware choice and architectural control. Broadcom’s preview of its Private Cloud Outlook 2026 report shows more than half of organizations are running or planning to run production inference in private clouds, with public cloud use declining. VCF 9.1 targets this shift with capabilities such as intelligent memory tiering and enhanced storage compression to improve workload density and reduce total cost of ownership, plus automated fleet operations that scale to thousands of hosts. It effectively becomes an AI operations management backbone for organizations standardizing on private AI.
Veeam’s Intelligent ResOps and Data Platform: Data Context as a First-Class Citizen
As agentic AI accelerates change, data resilience is becoming inseparable from AI operations management. Veeam’s new Intelligent ResOps solution, built on the Veeam DataAI Command Platform, aims to unify data context and recovery so teams can precisely understand and remediate AI-driven changes. Its DataAI Command Graph continuously maps data, users, permissions, AI agents, activity, and protection status, making it easier to see what matters most and what is at risk. Instead of broad rollbacks, organizations can restore only affected data after incidents, reducing disruption. At VeeamON 2026, the company also previewed Veeam Data Platform v13.1 and a DataAI Resilience Module, expanding this approach across hybrid and multi-cloud environments. By tying backup, governance, identity recovery, and AI activity into a single data governance platform, Veeam positions itself as the unified data trust layer for production AI deployment.
Toward Integrated Operating Layers for Agentic AI
Alongside these moves, newer players such as Xurrent are extending enterprise AI infrastructure with autonomous agents and an open Model Context Protocol server for service and operations management. Taken together, these developments show a market shift from standalone tools to integrated operating layers that span infrastructure, orchestration, data, and recovery. IBM’s focus on an agentic control plane, Broadcom’s private cloud foundation for AI, Veeam’s unified data resilience, and Xurrent’s autonomous agent stack all point in the same direction: enterprises need cohesive platforms that embed governance, compliance, and observability directly into AI workflows. As multi-agent systems grow more complex and regulations around data and sovereignty tighten, these integrated stacks will increasingly determine which organizations can move from experimental agents to reliable, large-scale production AI deployment without losing control of risk, cost, or compliance.