Indian finance leaders are embracing AI agents at a rapid pace, but new research from Avalara suggests the race to deliver measurable returns is exposing a growing governance problem. According to the company’s latest global survey, CFOs and senior finance executives in India are feeling intense pressure to prove AI investments are paying off—even if internal controls aren’t ready for the technology they’re deploying.
The report, “Agents of Change: How the Race to Deploy AI Agents is Outrunning Financial Governance,” surveyed finance leaders across India, the US, the UK, and Australia. While enthusiasm for AI agents is high worldwide, the findings indicate that Indian organizations may be taking a particularly speed-first approach, increasing the risk of compliance and audit challenges.
Speed Is Winning Over Governance
The survey found that 85% of Indian finance leaders face moderate or significant career pressure to demonstrate a return on investment from AI agent deployments. At the same time, 71% said organizational focus is centered on deployment speed, while just 8% reported that governance is prioritized over rollout velocity.
That imbalance is creating operational risks. Nearly one in four (24%) respondents admitted their organizations have not updated internal controls within the past year to account for AI agents making or recommending business decisions.
As AI agents move beyond experimentation into finance, tax, and compliance workflows, outdated governance frameworks could become a liability rather than an administrative inconvenience. Regulators are increasingly scrutinizing AI-driven decision-making, and organizations unable to explain automated actions may face greater compliance risks.
Accountability Remains Murky
One of the report’s more concerning findings is the lack of clear ownership when AI systems make mistakes.
More than one in four Indian finance leaders said accountability for significant AI errors is either unclear or assigned to no one—higher than the levels reported in the other markets surveyed.
Transparency is another weak spot. Ten percent of respondents said they are not confident they could provide regulators or auditors with a clear explanation of how an AI agent reached a particular decision. While that figure may appear relatively small, it stands out because it is significantly higher than comparable responses from finance leaders in the US, UK, and Australia.
For finance departments operating in highly regulated sectors, explainability is quickly becoming just as important as automation itself.
Dulles Krishnan, Vice President and General Manager of India Operations at Avalara, believes many organizations are modernizing technology faster than they are updating policies.
“While Indian enterprises are moving fast to automate, their internal rulebooks are being left behind. Running new AI tools on outdated compliance policies is a massive blind spot. CFOs need to ensure their risk frameworks are actually updated to monitor automated decisions before an auditor comes knocking.”
Skills Gap Adds to the Challenge
Governance isn’t the only hurdle. The research also highlights an expertise gap that could complicate AI adoption further.
According to the survey, 76% of Indian finance leaders say they lack dedicated in-house expertise capable of understanding how their AI agents actually work. That creates a difficult situation where organizations increasingly depend on AI for financial operations while having limited internal capability to validate, monitor, or troubleshoot those systems.
It’s a challenge becoming common across enterprise AI deployments. As generative AI and autonomous agents move deeper into business operations, demand for AI governance specialists, compliance professionals, and technical auditors is rising much faster than talent supply.
Trust Features Matter More Than New Capabilities
Rather than asking for more sophisticated AI, finance leaders appear to be prioritizing reliability.
When asked what would increase confidence in expanding AI agent deployments, respondents highlighted several trust-focused capabilities:
- 27% want outputs grounded in verified tax and financial data.
- 26% favor mandatory human review for high-risk decisions.
- 22% prioritize comprehensive audit trails documenting every AI action.
- 20% seek stronger vendor commitments around accuracy and accountability.
The responses suggest that enterprise buyers are shifting their attention from AI capabilities alone toward governance, transparency, and risk management—areas that are increasingly becoming competitive differentiators among enterprise AI vendors.
Why It Matters
The findings arrive as businesses worldwide move from AI experimentation to production deployments. Finance functions, in particular, are becoming prime candidates for AI automation because of their repetitive, rules-based processes. However, unlike customer service or marketing, mistakes in tax, compliance, and financial reporting can carry regulatory penalties and reputational consequences.
The report underscores a broader trend emerging across the enterprise AI market: organizations are no longer asking whether to deploy AI agents, but whether their governance frameworks are mature enough to support them.
For Indian enterprises, the next competitive advantage may not come from deploying AI the fastest. It may come from deploying it responsibly—with transparent decision-making, updated controls, and clear accountability built in from day one.
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