Making GenAI work in finance

The red-hot excitement about GenAI has moderated to a more pragmatic level. A practical approach—based on delivering tangible, measurable results—likely appeals to CFOs. And with some companies already seeing encouraging returns on their early GenAI investments, stragglers risk falling behind.

The capabilities of AI and GenAI have been advancing fast. As a result, CFOs may find themselves in a bind. Get out ahead of the curve, and you may make a misstep. Wait too long, and you risk falling behind. The challenge now is to find a sweet spot for measured AI adoption, a balancing act that’s different for each organization. The rate at which most companies make the GenAI transition tends to reflect the pace at which the organization can absorb change, rather than the advancing speed of technology.

As stewards of capital, finance leaders may feel driven to see a clear ROI before they greenlight major AI deployments in their departments. They’re likely to undergo a cycle of experimentation, typically involving small projects or proofs-of-concept (POCs), before even considering applying AI-enhanced productivity tools to discrete tasks. Initially, ROI may reflect reducing leakage from the business. For finance, this could mean measuring customer behavior, such as loyalty and on-time payment. But long term, the impact of AI will likely be on a much grander scale: an end-to-end overhaul of financial and operational processes.

So, once they’re ready, how can CFOs lay a solid foundation to help overcome foreseeable hurdles and accelerate GenAI’s creation of business value? Here are a few steps to consider:

• Prioritize data and digital fluency to build a culture of innovation and trust around AI
• Recognize the significant effort required to prepare data for AI
• Assess the current finance technology roadmap to identify where AI can accelerate capabilities
• Strengthen governance to scale AI capabilities while managing risks

CFOs and finance leaders can play a pivotal role in driving collaboration among C-suite leaders to enable the greater success of AI deployment and adoption. The effort should begin with a sound strategy and a few use cases to assess, and it should be underpinned by well-governed, accessible data. While it still may be early days for AI in finance, the potential for generating a return on a modest deployment of artificial intelligence is real.

 Check out our full article for more insights.