Danish companies can gain time, money, and quality by working with AI in financial planning. That’s why we give you three recommendations in this article for AI functions that can help improve your company’s FP&A processes – and you’ll also get more knowledge about what to be aware of when starting to use AI in your FP&A department.
If you are working with FP&A, you probably recognise the feeling of being under pressure as regards both time and overview – often to such a degree that you don’t have sufficient time to act as an analytic or as a support to the business because the process is so cumbersome and work-intensive.
Therefore, we encourage you to consider using AI as a work process in your FP&A department. AI can help you improve the accuracy of your prognoses and budgets, ensure the highest possible quality of your data, and automate many routine tasks taking time from more value-creating activities that demand analysis and strategic thinking. Used in the right way, AI can help your FP&A department transform from number crunchers into finance business partners playing a core role in the organisation.
And no matter which tool your FP&A department use, AI is probably only a few clicks away.
AI modules are, in fact, integrated in Excel, Google Sheets, and all major, specialised FP&A tools.
Therefore, we give you our recommendations for three general AI functions that it’s worth keeping an eye on since they may reduce your workload as well as increase the quality of your FP&A department’s deliveries.
1. Improve the accuracy of prognoses and budgets
AI can use historic data to predict future patterns, and it can help improve the accuracy of the financial prognoses and budgets made by your FP&A department. Depending on the volume of data, it takes somewhere between a couple of minutes and an hour to prepare a complete draft budget – somewhere between the time it takes to get a cup of coffee and have a job performance talk.
You can even give the AI model real-time insight into the financial data of your business, thereby enabling you to make decisions on a more accurate basis and to take action on possibilities that require shorter time for consideration.
2. Validate and ensure the quality of the data provided by your FP&A department
You can also use AI to ensure the quality of the estimates made by the FP&A department by having it check where your prognoses could do with a little extra attention.
Based on historic data, many tools can identify areas where your estimates are inconceivably high or low.
This function is, i.a., provided by the FP&A tool Workday Adaptive Planning, which uses a Machine Learning model to assess the data inserted in a forecast or budget by your FP&A department.
It’s a function that could potentially release much time and energy in a squeezed budgeting process, while at the same time increasing the quality of your estimate.
3. A chatbot to answer your questions
Spending time and energy answering questions from the rest of the organisation during the budgeting process may also be burdensome.
Since even simple questions often require time-consuming investigations and double-checking facts.
Consequently, your FP&A department could potentially save much time by creating a chatbot to be used as an ultra-fast engine searching through your financial data.
You can also design the chatbot to draw on other information from your business – for example, in the form of process descriptions, deadlines, and information from your ERP system.
If you couple, for example, an AI language model to the company’s historic data, it can, in milliseconds, answer questions such as “Who is our most profitable customer?”, or “Which products have had the greatest sales increase since 2021?” – questions that may seem simple, but the answering of which may quickly steal time, especially if the answers are to be found in several complex Excel sheets.
But do make sure that it is always an employee with the right competences who operates your FP&A department’s AI models.
Especially in connection with functions such as a chatbot, it is important to have somebody ensure the quality of answers and put them in the relevant context.
AI, Machine Learning and Predictive Analysis – what’s the difference?
AI, Machine Learning, and Predictive Analysis are areas that are closely related and three terms that are often used randomly. So, if you are confused, we do understand that.
Put in brief, AI is the wide common denominator covering everything related to the creation of systems and applications capable of imitating a kind of human intelligence.
Machine Learning is a sub-category of AI focussed on enabling machines to learn by themselves on the basis of data. Machine Learning is defined by the machine identifying data patterns and correlations through models rather than being explicitly governed by rules, as is the case with general software programming.
Finally, Predictive Analysis is a term used about the predictions made based on historic (and current) data, using the trained Machine Learning models.
What would it be an advantage to focus on?
No matter whether you are ready to press the ‘start AI button’ or whether you are still getting used to the idea, it is important to know that the use of AI also involves some issues that it’s worth considering beforehand.
If you choose to use AI in your FP&A department, your business must have a good data quality and confidence in your figures. Should AI be fed with incorrect figures, it will base its further work on these figures.
Before you start using AI, we also recommend that you consider who is responsible for the figures made by an engine. Therefore, we recommend that your FP&A department establishes a clear distribution of responsibilities beforehand or a process description to ensure that you check and assess the quality of the AI output before handing it on to other users.
Finally, it would be a good idea to remember to allocate resources for, for example, training, technical support, and legal assistance to ensure that your company’s use of AI complies with the relevant legislation in this area before implementing AI solutions.
Increased accuracy, higher quality, and time savings
To ensure that Danish companies don’t lag behind in technological developments, it is important to start using AI – also as regards FP&A. And, luckily, there are many advantages to be had. Briefly put, the key words are increased accuracy, higher quality, and time savings.
AI is eminent at recognising patterns – an ability it can use to present exact forecasts of figures based on patterns identified in your company’s previous budgets.
And by incorporating chatbots based on company data, you can quickly get answers to many of the questions frequently occurring during a budgeting process – questions that it is an important but time-consuming process to answer.
AI used in the FP&A function increases skilled and experienced FP&A employees’ possibilities of making better analyses and spending time on strategy-related work – while having algorithms do the repetitive, manual work.
Read this and other articles (in Danish) in our magazine Content here.
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When helping a Finance function digitalise and automate, we take pride in combining the applications and tools that will meet your specific demands. In a way so that the solution supports the data, processes and people working hard behind the lines.
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