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GenAI is evolving rapidly and has the potential to enable CFOs to deliver valuable new strategic insights and predictive analysis to their organisations.
In brief
- The ability to instruct GenAI using natural language means that advanced coding skills will no longer be required to run complex analytics.
- CFOs will need to broaden their data sourcing beyond the finance domain, tapping into other functions and departments for information that can inform forecasts and strategic direction.
- While GenAI outputs may not yet be fully trustworthy, they soon will be, and CFOs must be prepared for that.
Almost every aspect of the finance function has benefited from technology advances in recent years. Automation has freed up time to move beyond financial reporting and engage in the provision of strategic business insights and forecasting for the entire business. Those advances include artificial intelligence (AI), natural language generation (NLG), and optical character recognition (OCR). Many large organisations have been using machine learning and related technologies to assist in areas like fraud and anomaly detection, transaction processing, business forecasting and customer management.
However, we are now on the cusp of a potentially transformative leap forward as a result of the advent of generative AI (GenAI). This new technology has the ability to democratise data science and analytics and put coding skills in the hands of just about everyone with the ability to interact with it.
It will no longer be necessary for a CFO or finance team member to be skilled in specific programming languages or database query skills. Once they can explain in plain language what they want the GenAI to do, the technology should do the rest. It may no longer even be necessary to put all the data into an Excel spreadsheet. AI will be able to take structured and unstructured data from within the organisation as well as from external sources to provide various outputs like trend analyses and forecasts, with numerous variations based on factors like seasonality or user-defined future events. Having done so, it can offer best, mid and worst-case scenarios to aid C-suite decision making.
This capability, which was formerly the sole preserve of skilled data analysts and programmers, is now in the hands of everyone with access to GenAI and who has received basic training on how to interact with it and is willing to experiment.
Need to develop understanding of data science
Certain skills are required no doubt, not least of them the ability to understand accounts and financial reporting standards. Beyond that, CFOs and finance teams will need to become familiar with data science, at least to a small extent. This will not necessarily present a major challenge as finance professionals have been using business intelligence systems for many years. However, they will have to develop a much deeper understanding of the topic if they are going to uncover the next layer of value which lies within the data at their disposal.
Having the tools to carry out the analysis on your behalf is just one half of the equation, knowing what you want to achieve through the analysis is the other. The importance of “prompting” and the ability to do this well will become a key skill in extracting the most from these tools.
At the moment, GenAI is viewed as a separate tool that operates independently of other software systems. That will remain so for certain general applications, but increasingly it will become an integral part of the software systems used every day in organisations.
In future, CFOs and finance professionals will use AI to interact with those systems in different ways. They will use natural conversational language to create reports, run analyses, and produce forecasts. The skill will lie in knowing what questions to ask and recognising where the data’s potential value might lie.
Use of GenAI to be a competitive differentiator
A new approach to data gathering will also be required in many instances. CFOs will need to look beyond finance to other functions and departments to source data for use in forecasts and strategic guidance, and very importantly to understand those departments’ key needs. That will require an understanding of where data gets sourced from, how it flows from one system to another, where the bottlenecks lie, where data is leaking or getting lost, and what issues need to be addressed to improve data availability. Having access to that data from across the business and from outside the business in the form of external market reports and so on, will be of crucial importance to realising the benefits of GenAI in the finance function.
This applies to all areas of an organisation. Department and division heads will need to understand not just their own area of the business, but all business functions and ensure that data flows between them as appropriate and required.
GenAI is far from faultless, however, and trust is a major issue. For example, no CFO will be willing to sign-off on financial statements if the finance team does not know how to check the GenAI outputs they are based on.
Explainability is another challenge. If a certain system is being used to produce statements or reports, the CFO must be able to explain how it works and how it comes to its conclusions. And therein lies another issue, inconsistency. At present, you can ask GenAI the same question 50 times and get a different answer on each occasion. That may be acceptable for marketing content, but it certainly will not work for financial statements and forecasts, where trust and data integrity is of utmost importance.
Fortunately, GenAI developers and organisations integrating the technology into other software systems are addressing these issues and the technology is improving at a rapid pace. But it is still not at a stage where it can be fully relied on. Humans will need to be kept in the loop at all times to verify the outputs and ensure that the systems are not hallucinating or being creative when they should not be.
The use of GenAI by CFOs and finance functions to support strategic decision making in their organisations will soon be a competitive differentiator. This means that even if they are not currently using GenAI in their organisations, CFOs need to experiment with it, understand how it works, what it can do, and the value it can bring to the business.
More importantly they need to help instil an experimental culture within the organisation where employees at all levels are encouraged to bring forward ideas for use cases without fearing repercussions for aborted pilots or lack of investment. Prioritisation and piloting of potential use cases will allow organisations to swiftly assess where GenAI can deliver value internally, to customers and shareholders. CFOs who fully embrace this early-stage trial and error will ensure that they are not left behind when the technology evolves to a point where it can be trusted, is consistent in its outputs and is fully explainable.
Summary
GenAI has the potential to transform the way finance functions operate and the strategic insights and guidance that CFOs can bring to their organisations. To realise that potential CFOs will need to understand the business needs across different departments, gain access to data from across the organisation, develop basic data science skills, and perhaps experiment with the technology to understand how it works, how to interact with it, and how it can deliver value to the business.
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