The arrival of generative AI capabilities for finance operations has created a period of both optimism and uncertainty among many CFOs. Many finance leaders are turning to generative AI to help streamline tasks and automate workflows among their team members, with the goal of making strategic decisions that will impact the direction of their organizations. 

However, many CFOs say that generative AI experiments haven’t yet translated into the productivity gains that were expected.

Deloitte’s recent CFO Signals survey reveals that 70% of CFOs expect to see 1-10% productivity hikes within their finance department from generative AI. CFOs are prioritizing generative AI and are expecting workers to optimize the capabilities the technology can bring to their organizations.  

As we move from a period of AI experimentation to AI adoption, CFOs will need to ensure their organizations understand how to best use AI — either by upskilling current employees or by bringing in talent with generative AI skills.   

In fact, the CFO Signals survey reveals that 60% of CFOs say bringing in talent with generative AI skills over the next two years is either extremely important or very important. However, 61% of CFOs say generative AI is having either a minimal impact on their current finance talent model, or no impact at all. 

With the demand for talent with generative AI skills expected to continue to grow, 50% of CFOs said they anticipate developing existing talent to incorporate generative AI in their organizations, according to Deloitte. However, it should come as no surprise that there is a learning curve with adopting generative AI tools specific to finance. 

So how can CFOs ensure their finance professionals are best using generative AI capabilities?  

At Microsoft, our finance organization has been on this journey for years and was well positioned to adopt new capabilities and quickly see productivity gains. One of those tools is Microsoft Copilot for Finance, which empowers finance professionals to reduce the time spent on repetitive tasks and accelerate time to insight to drive the business forward.

With accounts receivable reconciliation capabilities in Copilot, our Treasury team is saving an average of 20 minutes per account, translating to an average of 22% cost savings in average handling time. And with data reconciliation capabilities, our financial analysts are spending an average of just 10 minutes reconciling data per week, down from 1-2 hours.  

GenAI tips for finance teams

Here are best practices for optimizing generative AI capabilities within the finance function, drawn from Microsoft’s experience:

• Confront hesitation: Leaders who may be hesitant about adopting AI need to understand that generative AI serves as a copilot, or an assistant, for their finance professionals. Generative AI is not meant to replace the work done by individuals. Outputs must be verified at the human level. Therefore, finance leaders need to optimize human energy for tasks that are uniquely human and user experiences that encourage and help users verify outputs.  
• Educate employees: Finance leaders should educate their employees about the need for human verification, the importance of having a "trust, but verify" approach to outputs, and the principle that humans are still the drivers. This will encourage finance professionals to view generative AI not as a tool to replace them, but instead to empower them. When employees feel empowered by generative AI, they will begin to see just how impactful it will be.  
• Encourage a growth mindset: Successful adoption of generative AI capabilities within a finance organization must begin with buy in at the top. This starts by fostering an environment that encourages a growth mindset. Finance employees need to be comfortable with the idea that they can continue to evolve their roles and skillset by embracing new ideas and technologies.
• Embrace the “flywheel of innovation”: To ensure employees feel empowered by AI, leaders need to create a safe space for functionality and culturally diverse teams to experiment in safe ways. Leaders should focus on creating a "flywheel of innovation" by giving employees access to new technologies, fostering experimentation, sharing learnings, celebrating wins, and then continuously repeating the cycle. Leaders and employees should have confidence that the generative AI capabilities they will rely on to support financial tasks perform very well, with limited variances. Encouraging employee participation in the experimentation process will lead to an increase in employee trust of outputs and make adoption feel more collaborative.  
• Understand generative AI is not a “magic potion” for data problems: Leaders should not view generative AI as a magic potion that is automatically going to solve all of the data problems they face. Finance leaders need to understand the importance of getting their data state in order to ensure they maximize the capabilities of generative AI. Leaders should continue to dedicate resources to help organize data. Creating rich and structured grounding data is crucial to optimizing generative AI capabilities for finance.   

There’s no doubt that generative AI will have a meaningful impact in redefining how finance professionals do their work. By putting these principles into practice, finance leaders and CFOs can encourage transformation while capturing the impact and benefits that support the strategic initiatives of their companies.