AI in invoice management

AI in Invoice Management: Benefits & Application

Today, there is no way around artificial intelligence, even in invoice management. According to a Statista survey in 2024, 68% of companies consider AI to be the most important future technology. However, only 9% of companies already use AI, and of these, 50% use AI in accounting, as a Bitkom study revealed.

So, how relevant is AI really? And what role does it play in invoice management in companies? What are the main benefits, and how can they be secured?

In this blog, we want to take a closer look at artificial intelligence and its role for financial managers and key users in companies. In addition, we will look at specific application examples and how financial software uses artificial intelligence for process automation.

What exactly is artificial intelligence?

Artificial intelligence (AI) describes the ability of a system to “intelligently” solve specific tasks and independently improve with human interaction over time. Ultimately, AI takes over a very tedious task for us: It organises things, understands them, and forms structures, relationships, and decisions.

There are different types of AI that have been used in financial processes for several years, including:

• Expert systems for rule-based automatic processing of repetitive tasks, such as assigning payment outputs.
• Machine Learning, where systems learn from historical data to handle processes more automatically or make predictions.
• Natural Language Processing (NLP) for chatbots in financial solutions.

In addition to these well-known types that primarily learn, solve problems, and assist in decision-making, there is a newer form of Generative Artificial Intelligence (Gen AI), which generates new content based on learned data. Currently, some of the most common Gen AI tools are ChatGPT, Neuroflash, and Gemini.

What role does AI play in invoice management?

Artificial intelligence is well-suited for use in finance and accounting because many repetitive tasks are particularly time-consuming. At the same time, the high volume of data provides a good basis for AI-based data analysis.
In accounting, AI can take over many tedious tasks, including:

• Recognising invoice contents from PDFs/images.
• Matching information between orders, deliveries, and invoices.
• Determining account assignments.
• Verifying tax rates.
• Researching documents.
• Translating texts from comments, documents, etc., especially in international contexts like shared service centres.
• Summarizing content from contracts, for example.
• Answering queries, including supplier inquiries or reporting.
• Creating emails, etc.

In invoice recognition alone, time and costs can be saved as AI enables automatic recognition rates of invoice contents over 90%.

What are the challenges in implementing AI in invoice management?

How well artificial intelligence works depends critically on the scope and quality of the data base. As mentioned above, large amounts of data usually arise in accounting. Careful maintenance of essential data, such as master data, is important so that AI can effectively learn and quickly deliver good results. Human input is therefore a crucial factor for the success of AI implementation.

However, what AI lacks is human intuition, so possible data gaps cannot be compensated by experience and personal assessments. AI is not flawless. Nevertheless, it often achieves better results overall. Since it is usually not exactly comprehensible how a machine learning-trained algorithm arrives at its result, AI is perceived as a black box. This can lead to acceptance problems with the new technology because how do I know if the results are reliable if I don’t know how they were determined? Random checks of AI results can help reduce this uncertainty. If the results are correct in repeated samples, these positive experiences strengthen confidence in AI. Employees benefit from support in their tasks and save time through research and the elimination of simple repetitive tasks. At the same time, they can contribute to the further development of AI capabilities by linking existing rule-based approaches with AI, ensuring continuous data maintenance, and making content from documents accessible to AI.

Data protection, liability, and compliance are other concerns associated with AI. Therefore, it is important to establish and communicate usage policies within the company and stay updated on regulations such as the EU AI Act. At the same time, AI can help with compliance and documentation obligations by identifying irregularities and potential compliance violations.

What are the main advantages of AI in invoice management?

Despite the various challenges associated with implementing artificial intelligence, its potential is particularly evident in the area of invoice management. The main benefits of AI include:

• Time savings: AI takes over time-consuming tasks, which is a real relief, allowing more focus on value-added tasks.
• Cost reduction: High automation rates increase the efficiency of accounting.
• Compliance: AI simplifies compliance with company-wide regulations and standards.
• Good supplier relationships: Faster processing times and payments, as well as effectively answering supplier inquiries through AI, improve relationships with suppliers.
• Budget & strategy: Planning is improved overall through the analysis of historical data.
• Scalability: AI-based processes and solutions are easier to adapt and thus form the basis for healthy business growth.

AI in practice: A case study in invoice management

The typical processes in accounting are all too familiar. Often, hundreds of invoices come in daily in paper form. These must be manually recorded and booked. This delays the approval process, and there is a high risk that documents will be lost or paid twice.

One of our customers, therefore, introduced AI-based software for invoice processing—in this case, SAP Invoice Management by OpenText—and digitised invoice receipt. This allowed the company to quickly get rid of paper and automate the validation and further processing of invoices. The most important lesson from this project was that change management is important to get employees on board. Only then can the necessary acceptance of the new technology be achieved, which is a prerequisite for good cooperation between AI and humans.

The practical application of AI can be illustrated with two case examples:

• Case 1: In an invoice from a new supplier, the total invoice amount could be read, but the details of the various items on the invoice were missing. If you copy and paste the details of one of the items into the input mask, AI can automatically fill in all the other items from the invoice using the “table autocomplete” function.
• Case 2: In an invoice from an existing supplier, the system has already recognised the company code and the invoice amount. However, the approver still needs to enter the account and cost centre. Instead of searching through old postings for the information, AI suggests these details from past transactions, speeding up the entry of information.