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- Report claims finance leaders prioritize Excel over AI for automation and security
- Cautious optimism defines finance's approach to AI integration challenges
- Regulatory compliance remains a significant barrier to AI deployment
There is a significant gap between the industry’s excitement about AI tools and the cautious reality of their implementation in finance, new research has claimed.
Rossum surveyed 470 finance leaders from the UK, US, and Germany to understand how they are navigating the current landscape of automation, and what challenges lie ahead.
It found finance leaders are cautiously optimistic, recognizing the potential benefits of AI but still wary of the associated risks - a point reflected in 58% of finance leaders still relying on traditional productivity tools like Excel.
Excel still dominates as the automation tool of choice
Leaders in the finance industry, known for handling sensitive and highly regulated data, face unique challenges when it comes to adopting AI.
Cybersecurity is a top concern for many leaders, as AI agents and systems introduce new vulnerabilities that cybercriminals can exploit.
AI also complicates compliance with GDPR and financial data protection law, and finance departments must establish clear guidelines to govern how these technologies are used.
AI or not, compliance and legal requirements have long posed a barrier to cloud based tools. Google Sheets, often touted for its cloud-native advantages, remains far less popular than Excel, particularly in larger enterprises.
While AI is seen as a powerful tool for automating document management, the survey found that 27% of finance leaders believe that the risks of implementing AI outweigh the potential benefits.
For finance leaders looking to embrace AI-powered automation, the report outlines several tactical steps. First, addressing the gap between current tools like Excel and more advanced AI technologies is crucial, and Rossum advise organisations to invest in training employees in AI implementation.
Additionally, building robust cybersecurity frameworks and ensuring compliance with regulations will help mitigate the risks associated with AI adoption, and establishing governance protocols, especially for generative AI, will be essential in navigating the complexities of maintaining ethical standards while implementing AI.