Why Finance Teams Are Still Wary of AI Investments

Three Reasons Finance Pros Don’t Love Your AI (Yet)

Companies are investing heavily in AI, aiming to improve finance operations. Yet, finance professionals remain hesitant. A survey of 1,500 finance experts in the U.S. and U.K. shows that while two-thirds of their companies have poured funds into AI tools, fewer than 20% rank AI as a top priority in their departments. Cost control and compliance still take precedence. Even with AI purchases approved, many finance teams aren’t leveraging AI fully within their own functions.

Where Finance Teams Stand: Three Key Concerns About AI

This gap between investment and actual use reveals a lack of trust. Finance pros worry about AI’s accuracy and risks. Many feel left out of AI discussions. Tech providers need to address these cultural and practical concerns if they want finance teams to embrace AI.

1. AI is Built for Younger Generations, but Finance Teams Are Multigenerational

Young workers adapt quickly to AI, often teaching themselves new tools online. However, only 38% of companies offer formal AI training, leaving many older employees behind. With five generations working side by side, finance teams have varied technical skills and learning preferences. AI products must be designed to support all users, not just early adopters.

2. Privacy and Accuracy Trump AI’s Efficiency Gains

Finance professionals approach AI cautiously, concerned about data privacy, security, and the risk of errors. Trust is earned when AI solutions meet strict compliance and performance standards. For example, companies need assurance that sensitive data won’t be used to train external models or shared with third parties.

Take optical character recognition (OCR) — a common AI feature in expense management. Initial accuracy rates between 60% and 80% frustrate users, especially when dealing with different currencies and languages. Improving these models by training them on diverse, real-world data and adding AI-powered quality controls can significantly boost reliability and trust.

3. Lack of Transparency Fuels Distrust and Staff Turnover

Trust issues don’t just hinder AI adoption—they impact retention. Nearly 25% of finance pros might leave their jobs if AI risks aren’t managed well, and 30% could quit if AI investments come at the expense of human development.

Finance teams want clear insights into how AI tools work. They need to know if these tools are allies or risks, especially when compliance is on the line. Showing where AI performs well and where human oversight is needed can transform skeptics into supporters. Dashboards or tools that track AI accuracy over time and explain outcomes help build this trust.

Finance is a high-stakes, heavily audited field. AI products must deliver clear performance metrics and respect users' expertise. Providers who listen closely and focus on meeting finance teams’ real concerns will build loyalty and long-term adoption.

If you’re a finance professional looking to deepen your AI skills and understand these tools better, consider exploring specialized courses that cater to finance roles. Training that bridges the gap between AI potential and practical application can make a big difference.

For more tailored AI education for finance professionals, check out Complete AI Training's courses by job role.