Finance teams embrace AI but 93% question the insights it generates, revealing deep trust crisis

Finance Professionals Embrace AI Despite Deep Trust Issues

A striking paradox is unfolding across finance departments worldwide. While AI adoption rises at an unprecedented pace, with more than 60% of finance teams increasing their use of real-time operational data over the past two years, 93% of finance professionals remain deeply concerned about the integrity and verifiability of AI-generated insights 1

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. This contradiction highlights a critical inflection point for AI in finance, where technological capability is racing ahead of trust and governance frameworks.

The joint report from ACCA (the Association of Chartered Certified Accountants) and CA ANZ (Chartered Accountants Australia and New Zealand), titled "Enabling Finance Insight: Bridging Skills and Data Gaps for AI-enabled Finance," surveyed 1,600 finance professionals globally and uncovered widespread anxiety about AI hallucinations, inaccuracies, incomplete datasets, lack of transparency, and bias 1

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. These concerns underscore the urgent need for responsible AI adoption practices that prioritize governance alongside deployment speed.

Finance Leaders Pressed for ROI Create Dangerous Governance Gaps

The pressure to demonstrate value is creating a speed-versus-safety dilemma that threatens to undermine long-term AI success. Research from Avalara reveals that 85% of Indian respondents feel moderate or significant career pressure to prove that AI agent investments are delivering ROI, with 71% reporting that deployment pressure focuses primarily on speed rather than governance 2

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. Only 8% say their organization prioritizes AI governance over speed, a troubling statistic that exposes regulatory vulnerabilities.

This rush has tangible consequences. In India, 27% of finance leaders said accountability for significant AI agent errors is unclear or sits with no one—higher than all other markets surveyed 2

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. Perhaps more alarming, 10% of Indian finance leaders admit they are not confident they could give a regulator or auditor a clear and complete explanation of an AI agent's actions. Furthermore, 24% have not updated their internal controls within the last year to reflect AI agents taking or recommending actions, while only 28% report that AI agent controls have been reviewed or tested by IT/cybersecurity teams 2

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Lack of In-House AI Expertise Compounds Trust Crisis

The skills shortages plaguing finance departments amplify concerns about AI reliability. The ACCA and CA ANZ research found that 72% of respondents reported having only basic or no GenAI skills, though 41% are pursuing AI training and upskilling on their own time 1

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. This gap between technology deployment and workforce capability creates a dangerous blind spot in financial operations.

Avalara's research reinforces this concern, revealing that 76% of Indian finance teams lack dedicated in-house expertise to understand how their AI agents work 2

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. "Running new AI tools on outdated compliance policies is a massive blind spot," said Dulles Krishnan, VP & General Manager, India Operations at Avalara. "CFOs in this market need to ensure their risk frameworks are actually updated to monitor automated decisions before an auditor comes knocking" 2

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Poor Data Quality and Integration Challenges Hinder Progress

Beyond skills gaps, technical barriers continue to impede effective AI deployment. Poor data quality and lack of appropriate skills were each cited by 42% of respondents as major obstacles, while 40% pointed to the challenge of integrating data from multiple sources 1

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. These foundational issues must be addressed before AI can deliver on its promise of transforming finance from a retrospective reporting function into a strategic enabler of enterprise-wide insight.

Despite these challenges, collaboration is improving. Almost 60% of finance teams now report close cooperation with data and IT teams, breaking down traditional organizational silos 3

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. This cross-functional approach is essential for building the data governance frameworks and audit trails needed to make AI outputs trustworthy and explainable.

Building Trust Through Governance and Upskilling

When asked what would most increase their confidence in expanding AI agents, Indian finance leaders consistently prioritized capabilities that reinforce accountability: outputs grounded in verified tax and financial data (27%), human review controls for higher-risk actions (26%), audit trails documenting every AI action (22%), and stronger vendor commitments around accuracy and accountability (20%) 2

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ACCA Chief Executive Helen Brand emphasized the critical role of governance: "CFOs and finance teams need to lead in the responsible adoption of AI across organisations, ensuring robust training and governance is in place. Critical thinking, sceptical validation and an ethical approach is vital" 3

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. CA ANZ CEO Ainslie van Onselen added: "AI is now a core part of the finance toolkit, but it's not a shortcut. CFOs and finance teams need to use it to sharpen judgement and generate real value, not just speed up old processes" 3

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The research makes clear that finance teams are uniquely positioned to lead responsible AI adoption because of their role in governance, data stewardship, and performance measurement. As organizations increasingly expect finance to provide forward-looking business insights rather than historical reporting, the function must strategically deploy AI technologies to generate value, not merely automate existing inefficiencies. Success will depend on investing in structured learning, strengthening collaboration with IT and data teams, and implementing robust data governance to ensure AI delivers trusted insights and measurable business value.