AI in Finance: What CFOs Need to Know

At its core, AI is a computer program or machine’s ability to learn from data at scale and then think, judge and act upon that data like humans do. While other technologies, like robotics, can replicate human action, AI replicates complex, nuanced knowledge work and thinking tasks. It does so using a variety of techniques, including machine learning, natural language modeling and rule-based learning.

Finance AI has made significant progress of late, but there is still room for growth. AI-based forecast models can evaluate data on multiple business drivers to project future revenue. Compared to a human analyst evaluating the same drivers in a spreadsheet, AI can process and analyze data faster and at much higher volumes. The depth of AI’s analysis is constrained only by the data available, the training from its designers and the directions of its users.

New use cases for AI in finance are continually emerging as the technology develops. The costs and benefits vary depending on the organization’s context and needs. For example, while one organization might gain needed insights into its cash forecasting by applying AI, an organization with fairly automated cash forecasting might not experience the same gains.

When choosing which finance AI use cases to pursue, evaluate the options that make the most sense for your organization. Assess the data sources aligned to a specific business problem before layering in the appropriate AI technique, and prioritize high-impact use cases based on intended outcomes, feasibility and scalability.

AI’s humanlike ability to make decisions often clashes with a conservative finance culture that relies on personal reviews to assure data accuracy. Organizations that make the most progress on AI take an intentional approach to building it.

To make fast progress on AI implementation and avoid common early pitfalls, apply the lessons learned from peer organizations. Organizations that succeed with AI in finance focus on training citizen data scientists, cataloging data assets and positioning AI as a co-worker. Make AI easier to adopt by making changes that leverage AI’s capabilities but also keep people in the loop where they are needed and where they perform well. Using AI to help employees perform better fosters acceptance, boosts job satisfaction and improves performance without driving fear that AI will take jobs away.