Artificial intelligence – without a doubt one of today’s hottest buzzwords. This little keyword sometimes carries with it massive expectations. Some market forecasters are already predicting a revolution or sketching out overwhelming threats a la Skynet. If you’re thinking of the 1984 hit “Terminator,” then you’re right on target.

But what exactly does the phrase mean? “The term artificial intelligence, or AI for short, stands for computer systems that imitate human intelligence. […]” This means it’s an umbrella term for a group of technologies that expand the classic boundaries of IT systems.

In my experience, and considering developments in digital invoice processing over the past few years, there seems to be a high level of maturity among the solutions available on the market today. Therefore, the development of products for invoice processing seems to be driven more today by detail optimization than by sensational leaps in development.

Everyday AI

Today, users consider web-based access and specialized apps that integrate mobile devices seamlessly into the review and approval process standard features. These kinds of services are broadly available today. No group company nowadays is without its own solution. Even among mid-sized companies, the majority of companies have already or are currently in the process of introducing such invoicing solutions. Ideally, these solutions would be dynamically linked to the company’s IT landscape and work seamlessly with back end systems.

However, AI-based methods have already snuck into our everyday lives, often without notice, becoming a natural part of our environment in both our private and professional lives. This also applies, but is certainly not limited to, solutions for electronic invoice processing. The trend might become clearest if we look at a few examples for how these methods have already become established.

Intelligent Capturing of paper documents

Let’s start with the electronic capturing of paper documents and provision of information for further processing. Even today’s widely available solution components offer amazingly good recognition rates and simple operation. These have continuously pushed the boundaries of technology over time. Just a few years ago, poor templates were a major problem, for example because they used thin and trans­parent paper or because folds, signatures, or stamps made automated reading of such documents more difficult.

Modern systems today capture a large percentage of these documents accurately. They do so by combining specialized algorithms: picture recognition algorithms use AI mechanisms to eliminate errors and optimize image quality. Error-tolerant systems (fuzzy logic) identify information even with incomplete input values. And multi-­dimensional vector systems learn through training with example data, for example with unknown address formats.

Learning from People Means ... Optimizing Algorithms

More and more, self-learning systems will increase their performance capabilities and recognition rates solely by observing human users while they work. This means the algorithms will optimize themselves through continuously comparing input in­formation with the information recorded by human specialists for further processing.

Next, I’ll take a closer look at the issue of intelligence, for instance in the context of incoming mail solutions or assistance systems.

Intelligent incoming mail processing

If an input channel is not used exclusively for incoming invoices, but also serves as an input gate for other information, we are dealing with the so-called mailbox or digital mail-room solutions. Here, too, AI-based solutions have been used for a long time. Classification of documents is no longer based purely on the recognition of key terms or isolated, formal criteria. A semantic analysis unit analyses and “understands” the content of a letter or an e-mail to the extent that not only the type of information is reliably detected, but also all relevant information is available for further processing. It is obvious that by reducing manual steps, process speed increases and costs can be saved.

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Mood recognition

The most developed systems not only capture the information received, but also recognise the sender’s mood (mood analysis). This information particularly supports applications such as complaints management and claims processing. Related techniques, for example, are already analysing certain channels in social media continually, and are predicting the success or failure of products with high accuracy and at a very early stage.

Speech control helps

When we look at solutions for accounting in practice, we find that despite all digital support auditing and approving invoices is often a time-consuming part of the overall process. Frequently, the reason is that the responsible persons are often and long in transit. The above-mentioned specialised apps for processing on mobile end devices already provide noticeable relief.

Further solutions from the field of AI are used here. From the private sector, we have known assistance systems such as Alexa, Google Assistant or Siri for some time. If we then use the ability of such systems to provide information in natural language meaningfully and respond to spoken instructions, it closes a further gap in companies’ financial processes. On the road and, for example, on car trips, between starting a speech-controlled play list and retrieving e-mails, what is there against also releasing some invoices and thus securing the cash discount savings?

Systems learn independently

In the near future, we will work with intelligent IT systems as intuitively as today with human assistants. Intelligent software systems will relieve us of today’s many, still manual or semi-manual steps, and communication will take place wherever possible through natural language. The systems are self-taught and support users actively.

By analysing large amounts of data (big data), transparency of available information increases and trends are detected at an early stage. Intelligent assistants use this information, network it in real time with numerous other internal and external sources of information, know their users’ needs, and actively assist users in their activities and daily decisions.

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About Thomas Schiffmann
Thomas Schiffmann and his team at EASY are responsible for product marketing and the EASY extranet. When he is not working, he enjoys cooking and spending time with family and friends. He spends his vacation mostly traveling and, in doing so, enjoys meeting interesting people.
Thomas Schiffmann
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