Transformation of Financial Planning and Analysis 

AI can be used to support and automate many of the tasks involved in FP&A, such as data extraction, analysis, and reporting. The latest developments enable Generative AI in connection with the forecasting tools to create reports, explain variances, and provide recommendations. The traditional approach was very time-consuming and focused on company’s internal data, hence, changes in the business environment were captured in hindsight.  

With the help of AI, FP&A professionals can combine outside-in view in the analysis and bring predictive insights as well as wider-ranging scenario analysis. This enables finance organization to focus on more strategic tasks, such as providing vision and recommendations to help businesses make better decisions from the financial standpoint.  

To succeed in a rapidly changing world, it is also a necessity to broaden the focus from internal data to capture signals of important changes in business environment early enough. It’s worth noting that industry plays a crucial role in fit for purpose predictive planning capabilities due to the market trends, demand, and competitive dynamics, which vary a lot from industry to industry. 

Lastly, from the wider perspective, a major benefit of using AI is that it can enable integrating financial planning with strategic planning, sales & operations planning as well as HR planning. Thus, companies can create and capitalise a holistic and unified company-wide forecasting and planning scheme, instead of executing more or less separate and suboptimal planning processes in silos. 

Practical Examples of AI-Powered FP&A 

There are many practical examples of how AI could be used in FP&A processes. Here are a few common ones: 

  •  Financial forecasting: AI can be used to forecast financial outcomes more efficiently and accurately by analysing historical data. For example, machine learning algorithms can capture complex relationships in the data by considering hundreds or even thousands of variables and interactions automatically. Linear and nonlinear machine learning models can be used to analyse relationships between various independent variables e.g. marketing spend, economic indicators and customer demographics, market trends, and other factors. Algorithms can also capture complex temporal patterns in data. With the modern technology, one can easily fit and continuously update separate forecasting models and their ensembles for, e.g., all products and time horizons, resulting in from tens to millions of individual models resulting in greatly improved forecasting. Many companies are already leveraging AI in their forecasting. However, implementations are often still small-scale “point solutions” that are not integrated in the forecasting process in a way that AI-driven “assistants” would operate alongside finance professionals, which is the recommended approach to reach the full benefits. 
  • Cash flow forecasting: AI driven data analysis and pattern recognition can be used to generate more accurate predictions of future cash flows based on historical values of internal and external influencing factors. Machine learning algorithms can consider various factors such as sales trends, payment histories, and economic indicators to minimize the need for manual intervention and therefore human planning errors and “gut feelings”. AI enabled solutions are also used in credit risk assessments to minimize the amount of credit losses impacting negatively to the cashflow. The importance of ethical considerations should not be underestimated when implementing and training algorithms to support customer risk profile related decision-making.  
  • Information retrieval and report creation: Many finance professionals use a substantial portion of their time in retrieving information from various sources and creating reports based on the findings. AI can be used to extract information (from financial documents) more quickly and easily. AI can also significantly improve information retrieval by using search algorithms that make information retrieval systems more adaptive and efficient. Over the past year, Generative AI has enabled the use of unstructured data in various tasks in a way that was not seen before. Many companies already use the technology for example in their internal data searches, which releases time from information capture to the actual analysis. Generative AI can also be used for creating the first drafts for the variance analysis and other reports, which further expedites the analysis.  
  • Financial data analysis: AI is playing a key role in financial analysis, particularly in extracting insights from large datasets. This is because AI algorithms can process vast amounts of data, find patterns, and generate insights that would be impractical or impossible for humans to achieve. AI systems can reveal hidden trends and correlations, spot anomalies and irregularities, and analyse variances between financial data points. AI algorithms can also provide explanations for their insights, allowing financial analysts to understand the rationale behind the identified patterns, anomalies, and variances. This helps finance professionals in identifying potential fraud, errors, or deviations from established trends that may indicate underlying problems or opportunities. This information empowers them to make informed decisions, optimise strategies, and enhance risk management. 

The Biggest Hurdles to be Tackled and Best Way to Get Started 

As AI is becoming more and more available and penetrating all levels of finance organizations, it’s important to understand the requirements and changes that come along. Especially considering FP&A, which is traditionally very much considered to be humans’ domain.  

First thing that we want to highlight is the change management aspects. A culture of innovation is essential for successful AI adoption. Finance organizations need to be willing to experiment open-mindedly with new technologies and learn from their mistakes. Experimental approach is often not inherent within finance communities and furthermore machine entering the human territory may induce change resistance. Hence, change management will be needed to successfully adopt AI in finance functions. 

It’s also good to understand and admit that AI introduces complex technologies, and technical expertise is needed to implement and use it effectively. In practice this means that many finance organizations need to strengthen the teams with some technical expertise.  

The availability of data, as well as their quality is crucial for AI. Data here including not only traditional, tabular financial data, but also unstructured, external data. Although technology is becoming more robust and can increasingly handle uncertainties in data, businesses need to ensure that their data is clean and accurate before using it to train AI models.  

However, it’s also important to get started. Given many finance organizations have already embarked on the journey, the quote from Alice in Wonderland is more topical than ever “Here we must run as fast as we can, just to stay in place. And if you wish to go anywhere you must run twice as fast as that”  


AI is transforming the way FP&A is done in companies. It can be used to support and automate many of the tasks involved in FP&A, such as data extraction, analysis, and reporting. With the help of AI, finance professionals can combine outside-in view in the analysis and bring predictive insights as well as wider-ranging scenario analysis enabling shifting the focus on more strategic tasks. As a result, finance organization can provide insights and recommendations to help businesses make better decisions from the financial standpoint. Hence, organisations that are early adopters of AI in FP&A will have a competitive advantage.  

If you are interested in learning more about how AI can transform your FP&A, please contact us today. We would be happy to help you get started. 

We hope this blog post was helpful. Please let us know if you have any questions. 


Heli Moilanen
Director, CFO Advisory
BearingPoint Finland

Jussi Ahola
Senior Technology Advisor, Data & Analytics
BearingPoint Finland



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