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By 2025, experts predict the total amount of data will reach an estimated 180 zettabytes. Your business, like many others, will be eager to invest in resources to understand that data for greater operational performance. At the heart of those future discussions are talks of how AI in finance will be an important part of your toolkit for gaining a competitive advantage within your market.
As a CFO, this guide will provide key context on the "why" behind AI in finance and some of its use cases for you and your FP&A team.
Two key issues are driving the need for AI in finance and the need for CFOs to become familiar with the abundance of AI tools at their disposal.
As our systems transition from analogue resources to digital and electronic formats (cloud computing), an increasingly infinite pool of data can inform and guide your business. The historic limits of our traditional data systems prevented companies from obtaining certain details useful for financial and operational planning.
You likely relied on manual data entry into Excel or other systems, which forced companies to prioritise certain data that you would consider essential for future planning (i.e., mostly financial data necessary for reporting on things such as cash flow and profit/loss statements).
Thanks to so much of our business happening online and via electronic systems, we can capture endless amounts of metadata and other non-financial metrics that provide essential context to your financial reports.
The problem for companies now, however, is again being able to use this data volume and diversity (this time due to the human limits of reviewing and making sense of it.) The widely accepted solution for this constraint on human capital is using AI to provide the necessary support.
AI can already do incredible things for reading, processing and sharing your data in helpful ways, but as this technology matures, the use cases will only continue to grow. 
Source: Gartner
For CFOs, the bulk of this data analysis is falling upon the shoulders of your FP&A team or finance department, creating a further need for AI tools to reduce the burden of manual tasks. It makes sense that finance groups are the natural hubs for general company performance data because their core function already focuses on reporting and analysis, just with an emphasis on finance.
Regardless of your business size or the size of your finance team, AI will likely provide some utility that makes the most of your available resources.
Generally, AI refers to the modelling of computer programming and machine systems to replicate the capabilities of natural human intelligence, such as problem solving, decision making and the ability to perceive (visual or audio comprehension). Particularly, you may think of two important subsets of AI that already have applications in finance: machine learning and natural language querying.
Machine learning (ML) refers to statistical methods and algorithms that allow computers to classify data, discover trends and insights, and make other uses of data mining. In other words, machine learning relies on the vast amounts of data accessible to the program and uses that experience to achieve a designed outcome.
Natural language querying (NLQ), sometimes called natural language processing (NLP), is a type of AI that allows computers to comprehend human speech and provide a natural response. You can think of NLQ capabilities as the bridge that allows us to quickly access the information your system has because of the work from its ML and other AI tools.
Source: Gartner (2020)
Below are some of the many ways AI is already paying dividends for those CFOs and business data leaders who integrate it within their FP&A process:
It's only the beginning for AI in finance, but now is the time to learn and adopt. Integrating AI tools and our FP&A software solutions is at the heart of our company's mission to help your company Plan To Grow.
