Broadcom says enterprises are crossing an AI “tipping point,” shifting production inferencing toward private cloud

Broadcom is making a pointed case that enterprise AI has entered a new phase, one where the economics and governance needs of production deployments are outweighing the convenience of public cloud. In its Private Cloud Outlook 2026 report, the semiconductor and infrastructure software company says the industry is reaching an “AI tipping point” for production inferencing, the step where models generate predictions from data after training.

Broadcom’s central finding is a measurable swing in where companies plan to run inference workloads. It reports that 56% of enterprises are running or planning to run production AI inferencing on private cloud. Over the same time period, it says the share for public cloud dropped 15 percentage points year over year, from 56% to 41%. The company frames this as a decisive shift rather than an incremental adjustment, describing 2026 as an acceleration beyond last year’s “cloud reset” toward a better balance between public and private environments.

The report attributes the shift to three drivers. The first is cost. Broadcom says public cloud environments are increasingly failing to address the financial realities of producing AI at scale. In a set of additional figures included in third-party coverage of the report, 31% of IT leaders cited cost as the top public cloud concern, and 97% said they see wasted public cloud spend. That same coverage also says 52% estimate that more than a quarter of their budgets are wasted, underscoring how many enterprises view inefficiency as a structural problem rather than an isolated misconfiguration.

The second driver is complexity. Broadcom’s report highlights that IT teams are dealing with operational overhead when they try to run production AI across environments, where data paths, security controls and performance tuning can become difficult to coordinate. The third driver is control. Broadcom links production AI demands to stricter governance needs, especially around security and the ability to keep workloads aligned with company policies.

Data protection and security appear as dominant themes for what is changing in enterprise requirements. Broadcom says its report identifies data protection and privacy (37%) as a major new demand placed on enterprise IT by AI, while security and control pressures are also cited among the top factors. It also suggests that jurisdiction and sovereignty requirements are reshaping architecture decisions, with privacy and data residency expectations playing a role in where workloads land.

The report’s framing implies that infrastructure decisions for AI are no longer only about getting access to compute or standing up prototypes. Instead, Broadcom argues that once AI moves into production, organizations prioritize predictable performance, tighter governance and a clearer way to manage data. That is the logic behind its conclusion that private cloud is where enterprise AI inferencing deployments are being directed for both security and scale.

Broadcom has continued to position its portfolio as aligned with private cloud and hybrid strategies, including VMware-based platforms and infrastructure software aimed at enterprise environments. In that context, the Private Cloud Outlook 2026 serves as a demand-side snapshot, attempting to quantify how enterprise IT priorities are shifting as AI scales beyond experimentation. The company also points to a government-related inflection toward private cloud adoption in an earlier update from 2025, indicating that the theme is not limited to commercial enterprises.

Broadcom did not provide, in the materials referenced here, a detailed breakdown of which industries or regions are responsible for the largest changes, nor did it disclose the methodology behind the enterprise survey figures in the excerpts available. It also did not specify whether the 56% figure for private cloud reflects fully dedicated private environments, single-tenant setups, or hybrid configurations managed as private cloud. Those gaps matter because they affect how closely the results map to specific architectures enterprises are actually adopting.