Australian finance leaders push AI agents despite gaps

Avalara has published research on AI agent adoption by Australian finance leaders, finding that governance is lagging deployment speed.

The survey covered 250 chief financial officers and senior finance leaders in Australia who had deployed, piloted, or actively evaluated AI agents in financial processes over the past year.

The findings point to a finance function under pressure to roll out AI tools in core processes while proving a return on investment. Almost nine in 10 respondents said they felt moderate or significant career pressure to show that spending on AI agents was delivering ROI, and half described that pressure as significant.

At the same time, 90% said their AI agent initiatives had already delivered at least some measurable ROI. Yet 59% said the main pressure around deployment was speed, while only 12% said their organisation prioritised governance over pace.

That gap also appears in how finance leaders assess suppliers. When evaluating AI-powered products, 23% said ROI was the top proof they wanted from vendors, compared with 17% for governance and control documentation and 18% for auditability and explainability evidence.

Confidence in explaining how AI systems behave also remains limited. Some 59% of respondents were only somewhat confident they could explain an AI agent's actions to an auditor or regulator.

Control gaps

The survey suggests accountability for errors is still not clearly defined in many organisations. Nearly one in five respondents, or 18%, said responsibility for a significant AI agent error would either be unclear or fall to no one.

Another 18% said the executive who approved the AI investment would ultimately be held personally accountable. That points to uncertainty over where responsibility lies when automated tools are used in finance, tax, and compliance work, where mistakes can be recorded, difficult to reverse, and potentially subject to regulatory scrutiny.

Skills are another weak point. Three-quarters of respondents said they did not have dedicated in-house expertise to understand how their AI agents worked, leaving them dependent on IT teams or external vendors.

Hugo Sarrazin, Chief Executive Officer at Avalara, said the pace of adoption needed to be matched by stronger oversight.

"Australian 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," Sarrazin said.

The findings add to a wider debate over how companies introduce AI into sensitive business functions. Finance departments are increasingly being asked to use automation in processes where records must be traceable and decisions may need to be explained to auditors, regulators, or boards.

Frank Cirone, Vice President of Commercial Strategy at Snowflake, said the issue was as much organisational as technical.

"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, 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," Cirone said.

Trust measures

Despite the governance concerns, the research indicates finance leaders do not want to slow adoption. Instead, respondents pointed to practical measures that would make them more comfortable expanding the use of AI agents.

Among the factors that would most increase confidence were audit trails documenting every AI action, cited by 31%, and AI agents operating within existing systems of record, selected by 32%. Another 30% wanted outputs grounded in verified tax, compliance, and financial data, while 25% pointed to validation against known compliance requirements. A further 20% highlighted vendor commitments around accuracy and accountability.

When asked which feature would be most valuable in financial operations, 37% selected audit-ready documentation for every AI-driven action. That suggests traceability may matter more to finance teams than broad claims about efficiency.

Jim Lundy, Founder, Chief Executive Officer, and Lead Analyst at Aragon Research, said those demands reflected the realities of using AI in tightly controlled workflows.

"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," Lundy said.

The research was conducted by Censuswide among finance leaders aged 30 and over in Australian companies with revenue of about USD $10 million or more across sectors including financial services, healthcare, manufacturing, professional services, retail, eCommerce, technology, and software. The respondent pool was limited to organisations that had already deployed, piloted, or actively evaluated AI agents in the previous 12 months.