Editor's note: Jim Caci is the Chief Financial Officer of AvePoint, a software provider headquartered in Jersey City, New Jersey, specializing in data management. The views expressed in this article are solely those of the author.

Chief Financial Officers (CFOs) play a critical role in driving investment, adoption, and security of artificial intelligence (AI), with responsibilities extending beyond the finance department to the entire organization.

AI implementation continues to pose significant challenges for CFOs and security teams. According toan AvePoint report, 45% of organizations experienced unexpected data exposure during AI implementation. Additionally, employees are likely to independently attempt to use AI without proper security guardrails, inadvertently putting sensitive data at risk.

As a cross-functional driver of risk management, compliance, budget allocation, operational efficiency, and strategic planning in new technology investments, CFOs are now responsible for ensuring AI is adopted and used securely.

With this in mind, CFOs should prioritize building a detailed AI implementation roadmap that includes the following key considerations:

1. Balancing adoption and risk

In close collaboration with IT and security teams, CFOs should first explore the following questions:

• Which applications can be safely used across the entire organization?
• Which open-source tools pose excessive data exposure risks?
• In which specific parts of employees' workflows are AI tools needed?

As leaders in technology investment decisions, finance teams should work closely with IT departments to first study how other organizations in the same industry utilize AI tools and whether they have created customized solutions to enhance productivity.

After fully considering the above factors, the executive team will be better equipped to decide which types of AI are suitable for different use cases based on risk tolerance and assessment results. Furthermore, through cross-functional collaboration with IT and cybersecurity teams, CFOs can build a more robust AI usage roadmap.

2. Ensuring appropriate data governance policies

Surveys show that during AI implementation, 88% of organizations claim to have information management strategies in place, but 44% lack fundamental measures such as archiving, retention policies, and lifecycle management solutions. Before considering AI adoption, teams must ensure that appropriate data governance and management policies are established. CFOs should work with IT departments to prioritize building robust data and information lifecycle management systems to enhance the quality of data that AI systems rely on and build upon. Automation policies can also ensure that employees only access relevant information.

3. Encouraging safe experimentation

Before full-scale rollout, CFOs at leading organizations are encouraging specific employee groups to test and experiment with AI tools under appropriate guidance. Inviting teams to participate as early adopters in AI pilots or test groups can help CFOs and the executive team gain deeper insights into the organization's personalized technology needs, informing future procurement decisions.

The adoption of generative AI is rapidly accelerating across industries, according toMcKinsey research, the proportion of organizations adopting the technology jumped from 50% in 2023 to 72% in 2024. This is not surprising, as AI technology continues to evolve, with its capabilities in processing large volumes of data and transforming business insights growing exponentially.

AI is now widely regarded as key to driving growth in 2025. The CFO office plays a vital role in this process, serving as the organization's core control hub for risk management, budgeting, operations, and strategy.

For this reason, every CFO, from Fortune 500 companies to startups, should immediately prioritize developing an AI implementation roadmap to ensure safe and efficient use, thereby driving bottom-line growth.