Cost-Benefit Analysis for Automating Excel Processes

The pressure to automate in the finance sector is growing. Many finance teams in SMEs want to modernise their processes to work more efficiently. However, a clear basis for decision-making to justify such a project internally is often lacking. A sound cost-benefit analysis for automating an Excel process in your SME is the crucial first step. It creates transparency for a business decision. A study by KPMG shows that 53 per cent of companies are already using artificial intelligence in accounting or are preparing to introduce it (KPMG 2025a). This guide offers a structured approach for controllers.

Recording the Costs Correctly

The total costs of an automation project go beyond the purchase price of a software package. A comprehensive view is necessary to evaluate the investment correctly. The costs can be divided into direct, indirect, and ongoing costs. A careful estimate helps to avoid unexpected expenses.

Direct costs are usually clearly identifiable. These include one-off licence fees for software or fees for external implementation partners. Detailed quotes form the basis of your investment appraisal.

Indirect costs are often underestimated. This includes the internal time spent on project management, requirements definition, testing, and documentation. Estimate the hours your employees will spend on the project. Multiply these by an internal hourly rate to monetise the indirect costs. This step is crucial for realistic planning.

Ongoing costs must also be considered from the outset. These include annual maintenance and support contracts. You should also plan for resources for future adjustments that may become necessary due to technological or regulatory changes. These recurring expenses are factored into the calculation of the total cost of ownership over the useful life.

Quantifying the Benefits — Beyond Time Savings

The benefits of process automation are multifaceted. Quantification is the key to demonstrating the value of the investment. The most obvious benefit is the time saved. According to a KPMG study, 37 per cent of companies using AI immediately observe significant time savings in transactional processes (KPMG 2025a). You can calculate this effect for your analysis.

To measure the time savings, identify a recurring, manual process. Ask the responsible employee to log the time spent on it over a month. Multiply the saved hours by the internal hourly rate. The result is a direct, monetary benefit for your analysis.

Another significant benefit is the improvement in data quality. Manual processes are prone to error. A typo can compromise the validity of a report. Automated processes perform tasks repeatably in the same, predefined way. This can prevent an entire class of errors and increases the reliability of financial data. This qualitative advantage is often convincing for management.

Also consider softer factors. Faster closing periods allow management to access information sooner. Automating monotonous tasks can increase employee satisfaction. Controllers can use their time for more demanding activities, such as analysis. These qualitative benefits strengthen the business case.

Payback Calculation as a Basis for Decision-Making

The payback calculation assesses the economic viability of an investment. It shows after what period of time the savings are expected to cover the initial costs. There are various methods, which are suitable depending on the complexity of the project.

The static payback period is the simplest method. It is calculated by dividing the investment costs by the annual savings. The result is the number of years until the investment has paid for itself. The method is easy to communicate but ignores the time value of money.

The dynamic payback calculation offers a more precise view. This method incorporates the time value of money. Future savings are discounted to their present value using a discount rate (projektmagazin 2018). The calculation is more complex but provides a more sound basis for decision-making, especially for larger investments.

The choice of method depends on the standards in your company. For an initial, manageable project, the static calculation is often sufficient. It provides a clear key figure to start a discussion with management. It is important to present the calculation transparently.

Presenting the Business Case to Management

A clean analysis is the foundation. The convincing presentation of the results is just as important. The goal is to provide management with a clear basis for a decision. Concentrate on facts and derive a clear recommendation.

Structure your argument logically. Start by describing the problem: the process, the effort involved, the weaknesses. Present the solution. Then, list the detailed costs and the expected benefits. Conclude with the payback calculation and your recommendation.

• The process to be automated and its weaknesses.
• The estimated one-off and ongoing costs.
• The quantifiable benefits, primarily the monetised time savings.
• The qualitative advantages, such as error reduction and faster reports.
• The calculated payback period as a key metric.

Present the facts soberly and professionally. The figures from your analysis should speak for themselves. A well-documented business case shows that you have carefully examined the project. It builds trust and increases the likelihood of a positive decision.

What You Can Do This Month

A complete cost-benefit analysis is a project in itself. However, you can start with small, concrete steps to lay the groundwork for your analysis.

First: Identify a suitable candidate for an initial automation. Look for a process that is clearly defined, repetitive, and rule-based. A good example is the consolidation of Excel files for a monthly report. Deliberately choose a process of manageable complexity.

Second: Measure the time spent on this process. Ask the person responsible to keep a simple log during the next month-end closing. This single figure is the most powerful lever for the benefits side of your analysis.

Third: Document the individual process steps. Record which data sources are used, what transformations take place, and what the final result looks like. This documentation provides clarity and is an important prerequisite for an automation project.

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