Mid-market firms stall at pilot stage for agentic AI

R Systems has published a research report on agentic AI adoption in the mid-market. It finds that most organisations remain in pilot programmes, even as a sizeable minority moves straight to agent-based approaches without following earlier stages of AI deployment.

The report, titled Agentic AI 2026: A Mid-Market Playbook for Adoption and Scale, is based on a survey of more than 200 leaders at mid-market enterprises worldwide. It maps adoption stages and offers an implementation framework focused on operational rollout, governance, and organisational readiness.

A key finding is the gap between experimentation and broad deployment. The study found 57% of enterprises are in a "pilot" stage for agentic AI-defined as controlled trials-while only 15% have reached a "scaler" stage, where agents have been operationalised across functions.

The research also suggests a less linear rollout pattern than is often assumed. It found 43% of organisations are "leapfrogging" directly to agentic AI rather than progressing through a sequential adoption path.

Trust And Policy

Despite early deployment, the report suggests confidence in the technology is relatively high: 64% of enterprises reported "high" or "very high" trust in agentic AI.

Governance, however, appears underdeveloped. Only 7% of enterprises said they have agentic-specific policies in place. Around 30% were described as significantly exposed because they operate with either generic AI frameworks or no policy at all.

The report frames this as a mismatch between the autonomy implied by agentic systems and the controls organisations need for auditability, security, and operational resilience. It also links policy gaps to workforce and ownership issues, including clear human oversight and defined responsibility for outcomes.

R Systems Managing Director and CEO Nitesh Bansal said the findings show where mid-market firms sit in the current wave of enterprise AI adoption.

"We are at a critical moment in the enterprise AI journey," said Nitesh Bansal, Managing Director and CEO, R Systems. "We are pleased to commission this report by Everest Group to not only give clarity on where enterprises stand in their agentic AI adoption, but also to provide a practical playbook for embedding AI into real enterprise environments, by balancing autonomy with accountability and driving measurable impact."

Operational Hotspots

Beyond adoption stages, the report highlights functions where agentic AI is already in use. IT operations is presented as the most scale-ready area, with examples including semi-autonomous incident triage, root-cause analysis, and runbook execution.

Software engineering is identified as another early deployment area. The report says the function is delivering nearly a 30% efficiency uplift, with gains across monitoring, requirements gathering, and testing and QA.

Customer support is also moving beyond basic automation. The report describes a shift from "deflection" to "resolution," with agents carrying out policy-bound actions such as refunds and entitlement changes.

In finance and accounting, it points to progress in structured workflows that require dual control, including reconciliations and close activities.

Industry Differences

The report argues that adoption varies by sector and correlates with digital maturity. Technology and telecom firms are described as scaling fastest. Banking, financial services, and insurance organisations are said to be moving more cautiously due to regulatory complexity, while healthcare organisations are largely characterised as remaining in exploratory phases.

These differences reflect how digitised and data-instrumented business processes already are, as well as the operational risk associated with automated actions. The sector view also highlights the practical constraints of integrating agentic systems into older application estates.

Scaling Challenges

The playbook outlines hurdles commonly encountered when moving from pilots into production, including integration complexity across fragmented legacy systems, immature tooling, ecosystem fragmentation, and requirements for security controls, auditability, and rollback.

It also flags limited governance maturity and workforce readiness gaps, linking readiness to AI oversight and data proficiency, as well as clearer ownership models for how automated actions are authorised, monitored, and reviewed.

Among its recommendations are outcome-led use case selection, governance embedded into production workflows, and scaling autonomy in tiers aligned to business risk. It also emphasises tackling integration issues, technical debt, and data integrity early in deployment.

The report also outlines an "ecosystem" view of providers involved in deployments, including hyperscalers, integrators, and specialist AI partners. It presents this as a practical procurement and delivery reality for many mid-market organisations that lack the internal resources to build and run all components themselves.

Akshat Vaid, Partner at Everest Group, said the research is intended to help organisations move from small-scale trials to execution.

"As organizations look to move from AI experimentation to execution, this report offers timely guidance on how to scale agentic AI responsibly. Our research commissioned by R Systems highlights what leaders must get right to convert early promise into sustained business value," said Vaid.