New research from Avalara reveals that while finance leaders are under increasing pressure to deploy AI agents and demonstrate return on investment, governance, accountability and internal controls are struggling to keep pace with adoption.
Avalara, an Agentic AI leader in global tax and compliance, has 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 US, UK, India and Australia who have deployed, piloted or actively evaluated AI agents in financial processes during the past year.
Key findings
- Ninety-two percent of respondents feel moderate or significant career pressure to demonstrate that AI agent investments are delivering ROI, with half calling that pressure significant.
- Half say their AI agent initiatives have delivered only limited measurable ROI to date.
- Seventy-one percent say the pressure to deploy agents is focused primarily on deployment speed.
Governance is falling behind the Agentic AI rush
- Only 7% say their organisation prioritises governance over speed.
- Thirty percent have not updated internal controls within the last year to reflect AI agents taking or recommending actions.
- Forty-four percent 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 capitalise on Agentic AI opportunities, but speed without accountability creates new forms of risk and speed without rethinking workflows limits ROI,” said Hugo Sarrazin, Chief Executive Officer at Avalara. “The organisations that realise 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.”
Pinpointing accountability
The research highlights questions about who is responsible for significant AI agent errors. For example, nearly one in four (23%) 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,” said Frank Cirone, VP Commercial Strategy at Snowflake, a cloud data platform company. “As AI agents gain access to financial and compliance workflows, organisations need to know what those agents can see, what they can do and when human approval is required. That kind of control must be built into the architecture, not added after the fact.”
Finance leaders prioritise 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 prioritised 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,” said Jim Lundy, Founder, CEO and Lead Analyst at Aragon Research. “As enterprises scale Agentic AI, the question becomes less about whether the technology can act and more about whether organisations 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.”
