AUTOMATIC today announced the release of its latest research report, Agentic AI Statistics for Finance & Business Services, providing a data-driven analysis of how autonomous AI systems are transforming financial operations and enterprise service delivery.
The report focuses on the rise of agentic AI—systems capable of independently planning and executing multi-step workflows—and positions the technology as a meaningful evolution beyond traditional automation tools. While earlier generations of software required constant human input, agentic systems are increasingly able to operate across entire processes with limited oversight.
“Most automation still depends on people to move work from one step to the next,” said Timothy Carter, Chief Revenue Officer at AUTOMATIC. “What we’re seeing now is a shift toward systems that can own execution. That changes how finance teams think about labor, throughput, and ultimately cost.”
According to the report, the broader market for AI in financial services is already substantial and expanding quickly. Current estimates place the market at approximately $35 billion, with projections exceeding $190 billion by the end of the decade. That growth is being driven by a combination of regulatory pressure, rising operational complexity, and the need for greater efficiency across high-volume workflows.
Within that broader trend, agentic AI is emerging as a distinct category. Rather than supporting individual tasks, these systems are being deployed across entire workflows such as financial close, reconciliation, compliance reporting, fraud resolution, and customer onboarding. In many cases, they are not just accelerating existing processes but fundamentally changing how those processes are structured.
The report suggests that a meaningful portion of work across financial services is already within reach of automation. Between roughly one-third and forty percent of tasks could be fully automated with current or near-term capabilities, while an additional share of work is likely to be augmented by AI systems working alongside human operators.
“Finance is one of the few environments where agentic AI makes immediate economic sense,” said Eric Lamanna, VP of Sales at DEV.co. “The data is structured, the workflows are repeatable, and the outcomes are measurable. When you deploy autonomous systems in that kind of environment, the return shows up quickly.”
Despite this potential, the report makes clear that adoption remains uneven. While the vast majority of organizations report using AI in some capacity, far fewer have implemented it at scale across core business functions. Most deployments remain limited to isolated use cases, leaving a significant gap between experimentation and full operational integration.
Carter noted that this gap represents the next phase of enterprise AI adoption. “There’s a difference between using AI as a tool and embedding it into how the business actually runs,” he said. “The companies that close that gap—particularly in finance—are going to have a structural advantage.”
The report also identifies the types of workflows most likely to be impacted. Processes that involve high volumes of transactions, repetitive decision-making, fragmented data, and multiple system handoffs are particularly well-suited to agentic AI deployment. These conditions are common across finance and business services, positioning the sector as an early proving ground for the technology.
Agentic AI Statistics for Finance & Business Services is based on a combination of industry data, third-party research, and AUTOMATIC’s internal analysis of enterprise AI implementations. The report is intended to provide a practical view of where autonomous systems are already being used and where adoption is likely to accelerate.
About AUTOMATIC
AUTOMATIC is an enterprise AI automation platform focused on deploying agentic systems across finance, operations, and business services. Along with LLM.co, the company enables organizations to design, implement, and scale autonomous workflows that reduce manual effort and improve operational efficiency.