Avalara: Finance Chiefs Rush AI Deployment Ahead of Controls

Finance departments are racing to deploy AI agents, but corporate oversight and risk controls are struggling to keep pace with the speed of adoption.

New research from tax and compliance software provider Avalara reveals a growing divide between executive demands for rapid AI deployment and the practical governance required to manage autonomous systems in critical financial operations.

The study, which surveyed 1,505 CFOs and senior finance executives across the UK, US, India and Australia, highlights a department caught between career pressure and risk management.

The pressure to deliver ROI

Mounting expectation from executive boards is driving rapid adoption. According to the data, 92% of finance leaders experience moderate or significant career pressure to prove that AI agent investments generate a clear return on investment (ROI), with half describing this pressure as significant.

However, rapid implementation has not yet translated into widespread financial gain. Half of those surveyed reported that their AI agent initiatives have delivered only limited measurable ROI so far.

Despite these mixed outcomes, speed remains the primary directive, with 71% stating that organisational pressure centers predominantly on how quickly agents can be rolled out.

This emphasis on implementation speed has left corporate governance behind. Just 7% of finance leaders said their organisation prioritises governance over deployment speed. 

Furthermore, 30% admitted their business has not updated internal controls within the past year to account for AI agents taking or recommending financial decisions.

The knowledge gap extends into regulatory readiness. Less than half (44%) of respondents expressed confidence in their ability to explain an AI agent's actions to an auditor or regulatory authority.

Hugo Sarrazin, CEO at Avalara comments: “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.

“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.”

Spotlighting accountability and control

The rapid integration of autonomous tools into financial workflows has created ambiguity around corporate accountability when errors occur. 

Nearly a quarter (23%) of senior finance leaders stated that accountability for a significant AI agent error would be unclear or sit with no one. 

Meanwhile, 16% believe the executive who approved the software investment would ultimately bear personal responsibility.

Internal technical expertise also remains a major bottleneck. The survey found that 76% of finance teams lack dedicated in-house expertise to understand how their AI tools operate, leaving them reliant on internal IT departments or external software 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,” says 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.”

Prioritising system trust and verification

Despite governance deficits, finance executives are not seeking to halt AI integration, according to the report. Instead, they are looking for infrastructure that allows them to scale automation safely.

When asked what features would provide the greatest confidence to expand AI agent deployment, respondents highlighted operational visibility and data integrity. 

Integrating agents directly within existing systems of record was cited by 27%, while 25% required outputs grounded in verified tax, compliance and financial data. A further 25% pointed to automatic validation against known compliance rules.

When evaluating key technical features, 30% identified audit-ready documentation for every AI-driven action as essential, alongside real-time monitoring of regulatory updates.