Agentic AI — software that can plan and carry out multi-step tasks with limited supervision — has moved from pilot projects to a standing line item in enterprise technology budgets. Several independent surveys published over the past year point to the same conclusion: the large majority of big companies are already running AI agents somewhere in their operations, and effectively all of them plan to expand that use in 2026.

From pilots to production

A widely cited survey of 500 senior executives at large enterprises found that roughly two-thirds were already using AI agents in some form, with around four in five describing their organization's use as either fully scaled or actively expanding across teams. Separate research from analyst firms tracking mid-2026 adoption put the share of organizations actively deploying agents across core operations at just over half, up sharply from a small fraction of that just two years earlier. Enterprise coding tools have moved particularly fast: the large majority of big companies now report using AI coding assistants in production rather than in testing.

The headline number that stands out across nearly every recent survey is unanimity on direction of travel: essentially all enterprises surveyed say they intend to expand their agentic AI footprint this year, even where current usage is still modest.

Where the friction actually is

What's changed is not enthusiasm but where the bottlenecks sit. Model capability is rarely cited anymore as the limiting factor. Instead, organizations consistently point to:

  • Integrating agents with existing systems and data sources, cited by close to half of respondents in one major survey as their top challenge.
  • Data access and data quality, a similarly common complaint.
  • Security and governance, which shows up as the top evaluation criterion when companies choose an agentic platform.
  • Measuring actual impact — one study found that while more than half of companies are running agents, only about a quarter have a reliable way to measure what those agents are delivering.

Other research paints a slightly more cautious picture of execution: a separate field report drawing on tens of thousands of user interactions found that a substantial share of enterprises still lack a clear starting point for agent deployment, and that a meaningful minority of pilots stall out before ever reaching production. The two pictures aren't necessarily in conflict — adoption intent and adoption maturity are different things, and 2026 looks like the year in which that gap becomes the central story.

What comes next

Analyst forecasts suggest task-specific AI agents will be embedded in a much larger share of enterprise applications by the end of 2026 than at the start of the year, up from a very small base in 2025. For most organizations, the near-term work is less about proving agents can work and more about building the governance, integration and measurement layers that let agents scale safely once they're already in production.