Finance Leaders are Racing to Deploy AI Agents Before Governance is Ready

Avalara have released new research revealing that while finance teams feel pressure to deploy AI agents as quickly as possible, governance, accountability, and internal controls are struggling to keep pace.

The report, “Agents of Change: How the Race to Deploy AI Agents is Outrunning Financial Governance,” surveyed more than 1,500 CFOs and senior finance leaders across the U. S. , U. K. , India, and Australia who have deployed, piloted, or actively evaluated AI agents in financial processes during the past year. Key findings Pressure to show value is mounting.

92%1 of respondents feel moderate or significant career pressure to demonstrate that AI agent investments are delivering ROI,with halfcalling that pressure significant. Half say their AI agent initiatives have delivered only limited measurable ROI to date. 71%2 say the pressure to deploy agents is focused primarily on deployment speed.

Governance is falling behind the agentic AI rush. Only 7%3 say their organization prioritizes governance over speed. 30%4 have not updated internal controls within the last year to reflect AI agents taking or recommending actions. 44% are only somewhat confident they could explain an AI agent’s actions to an auditor or regulator.

The findings reveal a finance function caught between executive pressure to accelerate AI agent adoption and the operational reality that those AI agents need to be managed with care, particularly in tax and compliance, where decisions must withstand regulatory scrutiny.

Finance leaders are right to move quickly to capitalize on agentic AI opportunities, but speed without accountability creates new forms of risk, and speed without rethinking workflows limits ROI. The organizations that realize the greatest value from AI won’t simply deploy more agents.

They’ll leverage agents with trusted data, governed workflows, and clear controls that enable automation with confidence. – Hugo Sarrazin, Chief Executive Officer, Avalara Pinpointing Accountability The research highlights questions about who is responsible for significant AI agent errors.

For example, nearly one in four (23%5) say accountability for a significant AI agent error would be unclear or sit with no one, while 16% believe the executive who approved the AI investment would ultimately be held personally accountable.

One of the challenges is a lack of available knowledge: 76% lack dedicated in-house expertise to understand how their AI agents work, relying on IT or vendors. Finance leaders are being asked to move quickly with AI, but governing agents requires a new combination of domain, AI, IT, and data governance expertise.

As AI agents gain access to financial and compliance workflows, organizations need to know what those agents can see, what they can do, and when human approval is required. That kind of control has to be built into the architecture, not added after the fact.

– Frank Cirone, VP Commercial Strategy, Snowflake Finance Leaders Prioritize Trust Alongside Speed The survey makes clear that finance leaders aren’t looking to slow AI adoption. They’re looking to scale it responsibly.

When asked what would most increase their confidence in expanding AI agents, respondents consistently prioritized capabilities that reinforce trust and accountability: AI agents operating within existing systems of record (27%) Outputs grounded in verified tax, compliance, and financial data (25%) Validation against known compliance requirements (25%) Vendor commitments around accuracy and accountability (24%) Audit trails documenting every AI action (23%) The capabilities respondents identified as most valuable were “audit-ready documentation for every AI-driven action” and “monitoring regulatory changes and applying updates in real time”, each selected by 30% of respondents.

AI agents are now moving into business processes that require trust, transparency, and governance by design. As enterprises scale agentic AI, the question becomes less about whether the technology can act and more about whether organizations can understand, control, and explain those actions.

In finance, where workflows are auditable and outcomes carry real business consequences, governance and explainability will become essential requirements for adoption. – Jim Lundy, Founder, CEO, and Lead Analyst, Aragon Research