Finance leaders are accelerating investments in agentic AI to automate tax, compliance, and financial operations, but governance frameworks are failing to keep pace, according to new research from Avalara. The study suggests that while enterprises are under growing pressure to demonstrate returns from AI initiatives, many organizations still lack the controls, accountability, and explainability needed to deploy AI agents confidently in highly regulated financial environments.

The race to deploy agentic AI across enterprise finance is exposing a growing governance gap, according to new global research released by Avalara, a provider of tax compliance and automation software. While organizations are moving quickly to integrate AI agents into financial workflows, many finance leaders acknowledge that internal controls and accountability mechanisms have not evolved at the same pace.

The report, “Agents of Change: How the Race to Deploy AI Agents is Outrunning Financial Governance,” surveyed more than 1,500 chief financial officers (CFOs) and senior finance executives across the United States, United Kingdom, India, and Australia. Participants had either deployed, piloted, or actively evaluated AI agents in finance functions during the past year, offering a snapshot of how enterprises are balancing automation ambitions with governance responsibilities.

Executive pressure is accelerating AI deployment

One of the clearest findings from the research is the mounting pressure on finance executives to justify AI investments. According to the survey, 92% of respondents reported experiencing moderate or significant career pressure to prove that AI agent deployments are generating measurable return on investment (ROI), while half described that pressure as significant.

Despite this urgency, measurable business outcomes remain mixed. Nearly 50% of finance leaders said their AI agent initiatives have produced only limited ROI so far, suggesting many organizations are still in the early stages of translating AI experimentation into operational value.

The survey also found that 71% of respondents said executive expectations are primarily focused on deployment speed rather than governance or long-term operational readiness.

This reflects a broader enterprise trend as organizations compete to incorporate AI into core business processes following rapid advances in generative AI platforms from technology providers such as Microsoft, Google, Amazon Web Services (AWS), and NVIDIA, all of which continue expanding infrastructure for enterprise AI applications.

Governance frameworks struggle to keep pace

While AI adoption is accelerating, governance appears to be lagging behind.

Only 7% of surveyed organizations said governance takes priority over deployment speed. Nearly 30% admitted they have not updated internal financial controls within the past year to account for AI agents making or recommending business decisions.

Explainability also remains a challenge. Forty-four percent of respondents said they are only somewhat confident they could explain an AI agent’s decision-making process to regulators or external auditors.

For finance departments operating under strict tax and regulatory requirements, these findings highlight a critical issue. AI-generated decisions increasingly influence tax calculations, financial reporting, and compliance workflows—areas where transparency and auditability are essential rather than optional.

According to Gartner, organizations are increasingly prioritizing AI governance alongside model deployment as enterprises move beyond experimentation into business-critical operations. Similarly, McKinsey & Company has reported that companies generating the greatest value from AI typically combine technology adoption with operating model redesign, governance, and workforce enablement rather than relying on automation alone.

Accountability remains an unresolved challenge

The study also points to uncertainty around ownership when AI systems make costly mistakes.

Nearly 23% of finance leaders said responsibility for a significant AI agent error would either be unclear or belong to no one. Another 16% believed accountability would ultimately rest with the executive who approved the AI investment.

This ambiguity raises important questions for organizations operating in highly regulated sectors where financial decisions must withstand scrutiny from auditors and regulators.

The report further reveals a knowledge gap inside finance organizations. More than three-quarters (76%) of respondents said they lack dedicated in-house expertise to understand how AI agents function, relying instead on IT departments or external technology vendors.

Industry observers argue that this shortage of AI governance expertise could become a significant barrier as enterprises move from pilot projects toward large-scale deployment.

Trust becomes the next competitive advantage

Rather than slowing AI adoption, finance leaders appear focused on making AI deployments more reliable and auditable.

Respondents identified several capabilities that would increase confidence in expanding AI agents across financial operations, including AI systems operating within existing systems of record, outputs based on verified tax and financial data, compliance validation, stronger vendor accountability, and comprehensive audit trails documenting every AI-driven action.

The two highest-ranked priorities—each selected by 30% of respondents—were audit-ready documentation for every AI action and real-time monitoring of regulatory changes with automated compliance updates.

These preferences indicate that enterprises increasingly view governance as an operational capability rather than simply a regulatory requirement.

For fintech providers, cloud platforms, and enterprise software vendors, the findings reinforce growing demand for AI solutions that combine automation with explainability, policy controls, and continuous compliance monitoring.

As agentic AI becomes embedded in finance functions ranging from tax determination to financial reporting, organizations are likely to evaluate platforms not only on productivity gains but also on their ability to deliver transparent, accountable, and regulator-ready decision-making.

Market Landscape

Enterprise AI adoption is moving beyond experimentation into mission-critical financial operations. According to McKinsey & Company, organizations that redesign workflows alongside AI implementation achieve significantly greater business value than those focused solely on automation. Meanwhile, Gartner identifies AI governance, explainability, and risk management as strategic priorities as autonomous AI systems increasingly influence regulated business decisions. These trends are driving demand for enterprise platforms that integrate trusted data, compliance controls, and transparent AI decision-making into financial workflows.

Top Insights

• Avalara’s global survey found finance leaders face intense pressure to demonstrate AI ROI, even as many organizations struggle to achieve measurable returns from agentic AI investments.
• Governance frameworks are lagging behind AI adoption, with relatively few organizations prioritizing oversight, updated controls, or explainability despite increasing regulatory expectations.
• Accountability for AI-driven financial decisions remains unclear in many enterprises, creating operational and compliance risks for finance executives and audit teams.
• Finance leaders increasingly prioritize audit trails, trusted financial data, and real-time regulatory monitoring as essential capabilities for scaling AI responsibly.
• The research highlights growing enterprise demand for governed AI platforms that balance automation, transparency, compliance, and operational efficiency across financial services.

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