Australian finance leaders are accelerating the deployment of artificial intelligence agents across financial operations even as internal controls, governance, and accountability frameworks lag behind, according to Avalara.
The report, titled Agents of Change: How the Race to Deploy AI Agents is Outrunning Financial Governance, surveyed chief financial officers and senior finance executives across Australia who have deployed, piloted, or evaluated AI agents over the past year.
The findings highlight intensifying career pressure to validate investments, with 88% of respondents reporting moderate or significant pressure to demonstrate return on investment (ROI)—and half characterising that pressure as significant.
Although 90% state their AI agent initiatives deliver measurable ROI, 59% note that organisational mandates focus primarily on deployment speed rather than control structures. Consequently, just 12% say their organisations prioritise governance over pace.
“Australian 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, CEO 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.” Hugo Sarrazin
The rapid implementation pace has created critical accountability gaps. Nearly one in five respondents (18%) admit that accountability for significant agent errors remains unclear or assigned to no one, whilst another 18% believe the executive approving the investment would bear personal responsibility.
Furthermore, 75% of finance departments lack dedicated in-house technical expertise to understand agent mechanics, relying instead on IT teams or external vendors. Additionally, 59% are only somewhat confident in explaining an AI agent’s actions to auditors or regulators.
“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.
“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 has to be built into the architecture, not added after the fact.” Frank Cirone
Rather than slowing adoption, finance leaders seek mechanisms to scale safely. Top priorities for increasing confidence include integrating agents into existing systems of record (32%), generating audit trails for every action (31%), and grounding outputs in verified financial data (30%). Moreover, 37% identified audit-ready documentation as the most valuable operational capability.
“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,” said Jim Lundy, founder, CEO, and lead analyst at Aragon Research.
“In finance, where workflows are auditable and outcomes carry real business consequences, governance and explainability will become essential requirements for adoption.” Jim Lundy
