Data security remains the top concern for corporate finance leaders, outpacing artificial intelligence, advanced analytics, and strategic planning, according to Protiviti's latest global finance trends survey. The findings, drawn from 902 finance professionals, show AI climbing the priority list but still trailing core operational responsibilities.
For the third straight year, the security and privacy of data earned the highest aggregate priority score - 7.6 on a 1-to-10 scale. Enhanced data analytics followed at 7.4, with process improvement and strategic planning each scoring 7.1. AI placed sixth at 6.9, up from 13th in the consulting firm's 2025 survey.
The report noted that AI's rise may actually be reinforcing data security's lead position. As finance teams adopt AI tools, the need to protect the underlying data grows more urgent.
AI enhances existing processes, not the finance function itself
Despite the buzz, AI has not yet reshaped the finance function. Only 1% of respondents said AI is driving significant business transformation in their finance organization. Instead, companies are applying AI to strengthen established workflows.
Financial forecasting leads AI use cases at 76%, followed by risk assessment and management (67%), process automation (56%), compliance and regulatory reporting (50%), scenario planning (44%), expense management (42%), and cash flow management (40%).
Measuring returns on AI programs remains difficult. Just 35% of respondents described themselves as at least moderately effective at gauging AI's ROI, compared with 45% for business-transformation initiatives. "This suggests that many organizations may be investing in AI without clear visibility into the actual, required or expected returns," Protiviti wrote.
Calls to action for finance leaders
Protiviti outlined several steps finance leaders can take to improve cost optimization and efficiency. The recommendations emphasize proven tools over speculative AI deployments.
The firm urged CFOs to continue investing in process transformation, automation, and cloud-based systems while "implementing and scaling AI where it strengthens those efforts." Finance teams should also build stronger spend, profitability, and performance analytics to reveal margin drivers by segment, customer, and market.
Data quality, governance, integration, and access must improve to generate reliable outputs from analytics, automation, and AI tools. High-volume, rules-based activities - financial close, reconciliation, reporting, expense management, invoice processing, and compliance workflows - present the clearest opportunities for automation.
Finally, Protiviti recommended rationalizing the finance technology portfolio. Companies should assess overlapping tools, underused licenses, fragmented workflows, and manual workarounds across the finance organization, partnering with IT to evaluate ROI implications across the broader enterprise. For finance managers looking to build skills in these areas, an AI Learning Path for Finance Managers covers budgeting, forecasting, and automation applications.
Why this matters for finance professionals
The survey clarifies where AI fits in the finance toolkit right now: not as a replacement for core functions, but as an accelerator for specific tasks like forecasting and risk management. The persistent difficulty in measuring AI's ROI, combined with the low percentage of teams seeing transformational change, signals that finance leaders should tie AI investments directly to measurable process improvements. Strengthening data governance and rationalizing the tech stack will determine whether AI moves from a top-10 priority to a genuine driver of financial performance. For CFOs navigating these decisions, an AI Learning Path for CFOs addresses financial strategy, forecasting, and risk analysis in practical depth.
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