2025 was the year of agentic AI baby steps. 2026 is the year it means business.

Last year, the industry buzzed about the potential of AI agents. Organizations experimented, piloted, and learned. But most stayed in the shallow end. This year, the data tells a different story: enterprises aren't asking whether to deploy agents — they're asking how to do it faster, more reliably, and at scale.

Yet a striking gap persists. While 65% of organizations say they already use AI agents and 81% report adoption that's scaling or fully deployed, the average enterprise has automated only about 31% of its workflows. The ambition is there. The infrastructure to deliver on it? That's where things get interesting.

ROI isn't the top concern — trust and integration are

When evaluating platforms for deploying AI agents, executives aren't leading with return on investment. Instead, they're prioritizing the foundations that make ROI possible: security and governance, seamless integration with existing systems, and reliable, consistent agent performance.

The implication is clear: organizations aren't deprioritizing business impact — they're recognizing that without governance, interoperability, and reliability, there is no ROI. Agents that can't be trusted don't get deployed. Agents that can't integrate don't scale. The platforms that solve these problems first will unlock the value everyone is chasing.

Efficiency first, then revenue — the agentic AI maturity curve in action

The survey confirms what early adopters have seen firsthand: agentic AI delivers its greatest value through time savings and operational cost reduction before it drives revenue growth. Seventy-five percent of respondents reported a high or very high impact on saving time, followed by 69% for reducing operational costs and 62% for generating revenue.

This maturity curve — from cost savings, to revenue generation, to enabling entirely new solutions at scale — is the natural trajectory of agent adoption. The enterprises furthest along are the ones that moved past pilots early and operationalized agents across real workflows.

Enterprises want to build on top of proven foundations — not start from zero

When asked how they prefer to orchestrate AI agents and workflows, 57% of organizations said they want to build on top of existing tools, compared to 43% who prefer building from scratch. The preference is even stronger in financial services (71%), construction (73%), and manufacturing (63%).

This signals a clear demand for platforms rooted in open source that offer extensibility and integration without lock-in — solutions that let teams move fast today while preserving flexibility and choice as the technology evolves.

Data readiness and talent gaps are the biggest obstacles — not lack of ideas

The top barriers preventing organizations from scaling AI that delivers real business impact are data readiness and integration challenges (35%) and insufficient talent or skills (33%). Technology limitations (27%) and budget constraints (25%) follow.

Notably, only 23% cite a lack of clear use cases as a main barrier. The meaning? Enterprises already know where agents can make a difference. What they need now are platforms that lower the technical bar, empower both engineers and business users, and provide the infrastructure to move from identified opportunity to production reality.

From pilot to production: the platform requirements taking shape

Across every data point in this survey, a consistent set of enterprise requirements emerges. Organizations need a platform that doesn't just help them build agents — it needs to help them operate agents at scale with the trust, governance, and adaptability that production demands.