57% of Mid-Market Firms Still Piloting Agentic AI, Study Finds

Most mid-market enterprises are still experimenting with agentic artificial intelligence rather than deploying it at scale, according to a new report commissioned by R Systems International Limited and conducted by Everest Group.

The report, Agentic AI 2026: A Mid-Market Playbook for Adoption and Scale, surveyed more than 200 mid-market enterprise leaders globally and found that 57 percent of organizations remain in the pilot phase, running controlled trials of agentic AI systems. Only 15 percent have reached the stage of scaling AI agents across business functions.

Agentic AI refers to systems capable of performing multi-step tasks autonomously rather than simply responding to prompts.

Despite limited large-scale deployment, enterprises appear increasingly confident in the technology. The study found that 64 percent of organizations report high or very high trust in agentic AI systems.

However, governance frameworks are lagging adoption. Only 7 percent of enterprises have policies specifically designed for agentic AI, while roughly 30 percent operate either under generic AI policies or without formal governance frameworks.

The report also noted that more than 40 percent of mid-market enterprises are bypassing traditional AI adoption stages in an attempt to accelerate competitiveness.

Early deployments are already delivering results in several business functions. IT operations has emerged as one of the most mature use cases, with organizations using semi-autonomous systems for incident triage, root-cause analysis and automated remediation. In software engineering, companies report efficiency improvements of nearly 30 percent, particularly in monitoring, requirements gathering and testing.

Customer support and finance functions are also beginning to adopt agentic AI for structured processes such as refunds, entitlement adjustments, reconciliations and financial close activities.

Adoption varies widely by industry. Technology and telecom companies are scaling agentic AI deployments fastest, while banking and financial services firms are advancing more cautiously due to regulatory requirements. Healthcare organizations largely remain in early exploratory stages.

The report identifies several obstacles to broader deployment, including legacy system integration challenges, fragmented AI tooling ecosystems, limited governance maturity and workforce readiness gaps.

Researchers said enterprises seeking to scale agentic AI must combine automation with stronger oversight frameworks, clearer accountability structures and better integration with existing enterprise systems.