The following is a guest article by John Parkinson, Senior Director of Emerging Technology at FreeClimb. The views expressed are solely those of the author.

I am often asked whether artificial intelligence can help busy CFOs with process automation and productivity gains in day-to-day tasks such as budgeting and financial reporting. As CFOs navigate the complexities of modern finance, they are increasingly exposed to claims about the potential benefits of AI, and like most senior business leaders, they cannot escape the hype that "generative AI solves everything."

With the ability to process massive amounts of data in real time, AI can accomplish work that is beyond human reach, but how will this help CFOs make better decisions, streamline processes, and drive business growth? The potential is real, but the path forward is not without challenges. Different types of AI, such as extractive and generative, can be used to provide valuable insights, improve decision-making, and support business operations and growth.

Extractive AI: Unlocking accurate and reliable insights from existing data

Extractive AI uses trained machine learning models to analyze existing datasets to identify patterns, trends, and correlations. This type of AI is particularly useful for CFOs who need to extract insights from large datasets, such as financial reports, market research, or customer behavior. Extractive AI is "deterministic"—as long as the underlying data remains unchanged, asking the same question multiple times will always yield the same answer. It does not occasionally "hallucinate" like generative AI.

This means it can deliver multiple benefits, including:

  • Improved forecasting: By quickly and accurately analyzing large volumes of historical data, extractive AI can provide timely insights into operational performance, helping CFOs make more accurate predictions about future financial returns.
  • Enhanced risk management: Extractive AI can analyze large internal and external datasets in real time or near real time to identify potential risks and opportunities.
  • Streamlined reporting: Automated reporting tools powered by extractive AI can reduce the time spent on manual data analysis, freeing up resources for higher-value tasks.
  • Knowledge capture: Extractive AI can collect and organize internal process knowledge that exists only in the minds of staff, simplifying succession planning and supporting efficient work practices.

However, there are also challenges to consider:

  • Data quality issues: The quality of extractive AI depends on the training data. Low-quality or incomplete datasets can lead to inaccurate insights and inconsistent reporting.
  • Limited creativity: Extractive AI is designed to analyze existing patterns and trends, which may not generate the innovative or breakthrough thinking needed for business strategy development.
  • Cost: Training extractive AI models on large amounts of data can be expensive, and if the application domain changes frequently and rapidly, periodic retraining may be required. Real-time inference can also be costly. Extractive AI does not always produce better results than traditional methods, and sometimes using non-AI tools for analysis may be more appropriate.
  • Security and privacy: Using external services for extractive AI may expose confidential business information to service providers. While many extractive AI tools can be used internally, the associated infrastructure costs and operational overhead may reduce their appeal.

Generative AI: Unlocking the potential for new insights through "creativity"

Generative AI refers to machine learning models that create new data or content after learning patterns from existing datasets. This type of AI has the potential to enhance financial functions by generating novel insights and ideas, rather than merely analyzing existing content. Similarly, the ability to examine and distill massive amounts of data helps generative AI tools provide "never-before-seen" ideas.

The benefits of using generative AI can be significant:

  • Innovative thinking: Generative AI can help CFOs think outside the box by generating new ideas and scenarios that might not otherwise be considered.
  • Improved scenario planning: By creating multiple possible outcomes for a given situation, generative AI can help CFOs develop more comprehensive contingency plans and design processes to better track and report unexpected changes in business conditions.
  • Enhanced strategic decision-making: Generative AI can provide CFOs with a range of potential strategies to achieve goals, rather than merely analyzing existing data.

However, there are also challenges that require serious consideration:

  • Lack of domain expertise: Generative AI models may not always understand the many critical nuances and complexities of financial processes, or the specific requirements of the regulatory environment. Public data used for training may not adequately represent the internal needs of a specific business or market.
  • Unreliable output: The quality of generative AI output is difficult to evaluate because it is often based on complex proprietary algorithms and assumptions that users cannot access. Models may "hallucinate"—producing outputs that appear evidence-based but are actually fabricated and supported by false data.
  • Cost: Generative AI models are expensive to train and can also be expensive to use. In domains where business environments change frequently, repeated training may be needed to maintain model functionality.

Considering the option of a hybrid AI approach

As CFOs navigate the opportunities and challenges brought by AI, a hybrid approach that combines extractive and generative capabilities may be key to success. By leveraging the strengths of each type of AI, CFOs can:

  • Efficiently analyze and effectively utilize existing data: Use extractive AI to analyze large datasets and identify patterns, trends, and correlations that can be used for day-to-day financial management.
  • Generate new insights: Carefully apply generative AI to create and test novel ideas and scenarios to inform strategic decision-making.

AI has the potential to revolutionize the finance function by providing CFOs with powerful tools for analysis, forecasting, risk management, and scenario planning. While significant challenges exist, a hybrid approach combining extractive and generative capabilities may be key to unlocking new insights and driving improved business performance, thereby enhancing the role of the finance function in driving business success.

Key takeaways

  • Extractive AI: Can be used to analyze existing datasets to identify patterns, trends, and correlations.
  • Generative AI: Can generate novel ideas and scenarios, but requires domain expertise and careful evaluation of outputs by human experts.
  • Adopt a hybrid approach: Combining extractive and generative capabilities may be key to unlocking new insights and driving improvements in finance and other business areas.

As CFOs continue to navigate the complexities of modern finance, a deep understanding of extractive and generative AI is essential for effectively leveraging these powerful tools. By embracing this hybrid approach, CFOs can unlock new opportunities for growth, innovation, and strategic decision-making in an increasingly complex financial environment.