For CFOs, figuring out how to best use AI in finance is a “right now” priority, not a “someday” priority. Nearly two-thirds of CFOs say their companies have a strategic goal over the next year to automate tasks typically performed by employees, according to a June survey by Duke University’s Fuqua School of Business and the Federal Reserve Banks of Richmond and Atlanta.
Among CFOs in the Duke/Fed Survey planning automation projects, a majority expect to implement AI to perform a wide range of tasks, including data reconciliation, the month-end close and data transformation (converting raw data into a unified format or structure). Those are complex, ambitious undertakings. However, they fall squarely within the competencies CFOs will need as their jobs become more strategic and data-driven, and AI adoption accelerates.
What skills do finance professionals need to hone if they want to thrive in this new, AI-augmented reality? Here are the five most important.
Industry Expertise
AI tools for finance are trained in accounting and finance tactics and theory. But nuanced industry expertise isn’t documented in textbooks or publicly available documents—the data sources that an AI can learn from. Finance professionals are the ones who can bring that knowledge, an integral component of decision-making. For instance, manufacturing finance’s focus on production processes and supply chains differs vastly from retail finance’s emphasis on sales analytics and customer behavior. Using industry-specific knowledge, a CFO can ask the right questions, guide AI model development and accurately interpret AI-driven outputs.
Mastery Of Core Finance Processes
AI can accelerate processes, but the finance team has to oversee their correct execution. Finance professionals need a deep understanding of areas such as closing the books, financial planning, revenue recognition rules and regulatory standards to ensure AI-generated outputs are anchored in sound financial practices.
Basic Coding Skills
Coding isn’t just for engineers. Basic knowledge of languages used frequently in financial modeling and AI, such as Python and R, can set a finance professional apart. A foundational understanding of coding allows for more informed conversations with data scientists and programmers. Moreover, ERP systems can offer robust APIs, allowing programmable access to finance data to automate data collection, cleansing and visualization.
Data Visualization Skills
Presenting complex, voluminous financial information in spreadsheets or tabulated reports to nonfinance executives can overwhelm them. Instead, finance professionals should master data visualization to communicate intricate information through data dashboards, interactive reports, charts and other visual representations. That requires expertise in data analysis and storytelling, as well as basic technical skill. A reasonable sense of design and aesthetics is also important.
Data Science Acumen
As with coding, knowledge of data science can prove beneficial when finance professionals work alongside AI to analyze vast amounts of data. A grasp of the fundamentals allows a finance executive to merge diverse datasets, choose the best-fitting model—a time series forecast, regression model or neural network—and appreciate the key drivers influencing outcomes. While AI streamlines this process, a finance professional’s basic knowledge is crucial for correctly interpreting and relying on AI outcomes.
Want to learn more about the skills finance chiefs need as AI reshapes the finance industry? Download the full CFO’s AI Survival Guide here.
