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The term robotic process automation is becoming increasingly important in the world of business. The technology that powers it helps to automate manual, repetitive, time-consuming and error-prone activities in companies by using what are known as software robots. This increases value creation and allows us to focus on our core business.

Just like in the first part of our blog post, the motto ‘low code instead of more code’ applies to this one, too. Instead of relying on elaborate developments, using RPA software enables users to develop automations on their own and manage workflows visually with the help of drag-and-drop functions – without the need for in-depth programming knowledge.

To expand the options for automating business processes, such as to process unstructured data more effectively, RPA can also be combined with intelligent AI functions (machine learning and analysis capabilities as well as cognitive technologies such as image recognition and natural language processing).

We’re talking about intelligent process automation. That is, extending the capabilities of RPA by using AI or other related technologies in a low-code development environment.

Intelligent process automation

Intelligent automation is a combination of RPA spectrum and artificial intelligence (AI) technologies that together enable rapid end-to-end workflow automation.

By using AI and low code, it is possible to extend the capabilities of RPA and develop intelligent applications. In our view, the main advantage is that business processes – whether simple or complex – can be automated quickly and cost-effectively using the combination.

Which challenge is intelligent process automation particularly suitable for solving?

Despite the much-emphasised digital transformation, customers are often still doing manual, paper-based work at their office. To bridge media disruptions, they manually transfer data into Excel or merge information from different sources.

Processing documents manually means going through each document, checking the fields, analysing the information and making a decision. These processes are slow and inefficient.

Speed isn’t the only problem. Resources that could be used for more strategic or business-impacting projects are also tied up, resulting in high opportunity costs.

On a similar note, mistakes are made that can potentially lead to making the wrong business decisions or even – let’s just imagine if there are number errors – high follow-up costs.

Intelligent process automation helps to minimise these risks because a software robot works perfectly, 24/7 and without getting tired. According to, companies that have already realised solutions report

  • 1. A reduced risk of error of up to 52 per cent.
  • 2. Cost reductions of up to 35 per cent in manual processes (for example in document processing).
  • 3. A reduction in process cycle times of up to 85 per cent.

We’d also like to use this post to show you the benefits of intelligent process automation using a simple use case that we quickly developed.

Use case – automated invoice posting in SAP with UiPath

Our potential client receives a large number of invoices in various formats and drafts each month and has to post them in its systems.

The number of invoices is a challenge. But even worse are the manual and labour-intensive steps required to process each invoice. The employee has to open individual e-mails with the invoices included as attachments, find the ID of a supplier in SAP and manually extract the data from the invoice to finally enter and post it in SAP. Following the automation motto ‘high potential’ and ‘low complexity’, this or a similar workflow can serve as an ideal entry point into the world of intelligent process automation.

To automate the process in this case, we use the low-code RPA platform UiPath and the UiPath Document Understanding AI framework for smart document processing.

This means that using RPA, low code and AI results in transformation gains for a company on several fronts:

  • Process cycle times are reduced – the average document processing time drops from five minutes to less than one minute.
  • High automation efficiency with a low error rate and thus improved processing quality.
  • Acceleration of the return on investment.
  • Development time is reduced – the entire solution takes less than a week to develop.
  • No investments in the development of AI models because of prefabricated Document Understanding AI components.
  • The supplier can send the invoices as attachments in e-mails or the invoices can be saved directly in a folder. There are several possible scenarios for delivering invoices. The invoices themselves can have different layouts and formats. These include handwritten annotations, signatures or check boxes, different file formats such as PDF, PNG, GIF, JPEG, TIFF, BMP, and so on, skewed or rotated, incoherent or low-resolution scans of documents. The robot has the right tools for processing.
  • The robot checks e-mails or folders (e-mails in our case) for new invoice receipts and extracts the invoices.
  • With the help of the UiPath Document Understanding component, the robot extracts the required data from each individual invoice, such as the date, number or total of the invoice, as well as data from tables (such as quantities or amounts).
  • The robot then goes through the customisable validation process. The employee can validate the data and edit it, if necessary, if there are inaccuracies, a lack of confidence or exceptions.
  • The AI models can be re-trained for Document Understanding based on the human corrections (the more staff work on the data, the more effective the models become). This improves the accuracy of the output over time.
  • Validated invoice data is placed in a work queue and then subsequently posted directly by the robot in SAP.
  • Invoices that have not passed validation are sent to the employee for manual processing.

Key facts about the development

• The advantages of this solution are based on an integration of RPA, a low-code solution and AI deployment.

• UiPath makes this development possible in just a few days – through drag-and-drop options to incorporate the key features, among other things.

• The Document Understanding component uses the out-of-the-box AI solutions with pre-trained machine learning models

The use case we’ve outlined above for posting invoices with SAP shows just one of the countless possibilities offered by one intelligent process automation. We can transfer the same solution to different document types and integrate it into a wide variety of systems (ERP, CRM or CMS, for instance). We achieve this thanks to the direct responsiveness of the UI elements.


The example we’ve presented clearly demonstrates that intelligent process automation – based on low code and AI – extends the possibilities of workflow automation significantly beyond the traditional areas of RPA application.

By introducing intelligent process automation, companies can use this combination of technologies profitably and achieve long-term competitive advantages.

Our vision is to provide our clients with support from the initial evaluation, to implementation, to scaling within the company. UiPath is another technology that helps us do this.

If you have any questions or want to learn more about low code, AI, RPA and/or intelligent process automation, please feel free to get in touch.

Would you like to learn more about exciting topics from the world of adesso? Then check out our latest blog posts.

Other parts of this series

Picture Dr. Zeljko Dzunic

Author Dr. Zeljko Dzunic

Dr. Zeljko Dzunic is a Senior Consultant at adesso Austria. His consulting focus is on the promotion, support and development of AI applications in various areas and AI-based intelligent low code solutions. In addition, he is involved in the use of AI in Robotic Process Automation.

Picture Matthias Proschinger

Author Matthias Proschinger

Matthias Proschinger is an associate consultant at adesso Austria. His work focuses on the topic of low code and process automation. In recent years, he has gained relevant experience in the financial services sector and wrote his master's thesis on the topic of AI predictions for student success.

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