AI in Business Controlling: How to free up more time for value creation – and get started quickly

The Business Controlling function is designed to ensure data quality, deliver insights, forecasts and strong sparring to the business. Yet many find that daily work – and especially the month-end close – disappears into data extraction, Excel spreadsheets and repetitive reporting.  

At the same time, AI has become significantly more accessible and can now be connected directly to financial data and processes. The question is no longer whether the technology works, but how you use it to get more Business Controlling and less "data processing" for the same salary budget.   

Therefore, in this article you will get:  

Taking a sober look at where Business Controlling typically spends its time, there is a clear pattern.  

 
1. Heavy data collection and data validation process  

Data tasks take up a lot of time. Data is extracted from ERP, CRM, sales systems and other sources, compiled in Excel and modelled until it resembles something usable.   
Once the data is ready, all the control questions follow: Are these the right figures? Do they match last month's report? Have we remembered to clean for one-off items and incorrect entries? This means that a lot of time is spent simply arriving at a figure you dare to rely on.  
 

2. Manual production of standard reports  

Monthly reporting, management reporting and various performance overviews are often based on the same structure month after month – more and more is supported by BI solutions, but much is still handled manually, both in terms of data, formatting and distribution. In other words, Business Controlling spends many hours reproducing the same format with new figures.  

 
3. Time-consuming administration of financial processes  

Month-end close, forecasts, budget rounds and collecting input from the business. Processes that are all necessary, but often iterative and cumbersome to manage without a certain degree of standardisation. Many teams therefore find that a large part of their workday is spent on follow-up, chasing input and managing deadlines across the organisation.  

 
4. Limited time for analysis, insights and business partnering – the tasks where value creation is greatest  

Finally, there's what Business Controlling actually thrives on, but paradoxically often has the least time for – namely analysis, insights and business partnering.   
This is also where value is created, for instance when a skilled partner can challenge an investment case, spot a structural change in the margin or translate the figures into concrete recommendations for management.  
 

The four challenges form a clear pattern: Too much time and too many resources are spent on manual, repetitive tasks, leaving too few hours for analysis work and sparring with the business.  

The question is therefore how AI can concretely lift these tasks out of your hands, so Business Controlling can focus more on value-creating work. That's what we'll address now. 

AI cannot replace Business Controlling's business understanding and judgment, but it is extremely strong at precisely the types of tasks that today take up a lot of time in daily work – and less in the value equation – as outlined in the previous section.  

 
1. A far more agile and automated data foundation 

Modern AI is strong at finding and creating coherence in data. Because AI can code, it can, for example, translate a common business question into an SQL query, retrieve data from the relevant tables and deliver a finished data foundation that would otherwise have been built in Excel.   
Requests like "show sales per month for the last 12 months, cleaned for a specific one-off effect" can actually be turned into an SQL query, executed and returned in a few seconds. This reduces the need for manual data processing every time someone asks for a "small" analysis.  

 
2. More efficient ad hoc responses without heavy BI processes  

Secondly, AI can function as a data retriever. If the company has a simple data layer – typically 20-30 well-described tables for e.g. financial accounts, customers, sales and costs – AI can understand the structure and translate questions into concrete data extracts. This makes ad hoc analyses and small mini-requests far faster and less dependent on heavy IT or BI processes.   
What previously took half a day with Excel and cube extracts can in many cases be something you chat your way to in a few minutes.  

 
3. Better first drafts for analyses and hypotheses  

Thirdly, AI is strong at analysing and finding patterns and outliers – for example in connection with month-end close. On a dataset of monthly sales figures, AI can quickly identify months that stand out and suggest possible explanations based on the knowledge that is available or has previously been through the AI solution.   
This doesn't replace professional judgment, but provides a qualified first draft that both saves time and can inspire new angles: "Here are the months that stand out – and some obvious hypotheses as to why."  
In addition, AI remembers all the data and all the input it has received, and thus becomes a better and better solution over time, because it expands its knowledge base from time to time.  

 
4. More streamlined and consistent reporting  

Finally, AI is good at generating text and reports based on figures. This makes it particularly well-suited for use in monthly and annual reporting, which often consists of the same structure and repetitive processes.  
Here, AI can write a first draft based on the figures and previous reports for e.g. introduction, explanation of developments or comments on variances – in English, in the company's own tone and in a format that resembles previous reports.   
This can both increase the pace and create more consistent quality in communication to management.  

 
This is where the "why" really emerges: By letting AI handle automated collection, initial analyses and reporting drafts, the Business Controlling function can shift time to what requires human judgment, namely to challenge, prioritise and set direction together with the business.  

The short answer is: it requires less than many think. You don't need to start a multi-year transformation programme or develop your own AI model from scratch. Instead, you should leverage the maturity that already exists in the pre-trained models and configure them to the company's reality.  

 
In practice, there are two tracks that can – and should – live side by side.  
 

Firstly, you can get started immediately by using a secure AI chat as a virtual assistant. Here the requirements are relatively simple: a tool that meets the company's data security requirements, some initial exercises so it's trained for you specifically and your context, and a team that's willing to experiment.  
 

In this model, you continue to work with the Excel files and report extracts you already have. You let AI help analyse data, suggest tables and visualisations, and write first drafts of the text that surrounds the KPIs in the monthly report. You can also have AI perform simple outlier analyses across periods or customers and give an indication of where you should look more closely.   

The value can typically be felt within a few weeks, because no new systems need to be built – it's primarily about changing the way of working and bringing AI in as a natural tool in the monthly process. 
 

Secondly, once the first experiences are in place, you can lay a more robust foundation by connecting AI to a simple data layer. Here the requirements are slightly more technical, but still manageable: a data warehouse or a set of central views with finance and sales data, typically 20-30 tables that are well-defined and related. You need to know what the tables contain, how they relate to each other, and what the most important fields mean. That description – the taxonomy – is in reality just as important as the technology itself.  
 

On top of that, you can build a Business Controlling chatbot that is both connected to data and configured to the context. Here it makes sense to be conscious about the "configuration" of the AI: What role should it have? What type of tasks should it help with? What tone and structure should it write in? By being explicit about the role – for example "you are a partner in an international manufacturing company who writes in Danish and follows this reporting structure" – the output becomes significantly more usable and consistent. 

The same data foundation can simultaneously be used to have AI write drafts of reports, variance analyses and explanations of performance directly based on live data. If you supplement this with previous reports as reference, AI can also learn to write in the company's own style and structure. Experience shows that the first concrete solutions can be in operation within one to three months, depending on how far along you are with data already.  
 

An important element is teaching the AI to stress-test its own output, which also minimises the risk of hallucinations.  
In practice, this means you design the solution so the AI actively assesses data quality and coverage before it responds. If data is missing, incomplete or doesn't support the analysis being requested, the AI should respond: "I cannot answer with certainty because data for Q2 is missing", or "There appears to be a data break in this period – this should be addressed before the conclusions are used."  

It can also mean that the AI compares new figures with previous reports and itself flags if something doesn't align with what was reported three months ago. In this way, AI doesn't just become a generator of answers, but also a critical player in data validation and quality control.  
 

Perhaps most importantly, what it doesn't require: it doesn't require Business Controlling to become data scientists themselves, and it doesn't require you to design your own large language model. The models are already pre-trained and understand general finance and controlling. The task lies in defining the challenges you want to solve, configuring the AI to the company's context, ensuring a minimum of data structure and governance – and training the solution to both deliver answers and be honest when the data isn't sufficient.  
 

AI doesn't change the fact that Business Controlling must understand the business, challenge assumptions and translate figures into decisions. But it does change where the time is spent.   

Because with AI as the engine for data collection, data handling and first drafts of analyses and reports, the function can shift focus from heavy data processing to what creates the greatest value: qualified sparring, better decisions and stronger business partnering.