On February 25th we kicked off a series of community calls with regards to SAP AI Business Services. We started off with highlighting examples of different use cases in which the SAP AI Business Services can be leveraged to achieve automation and efficiency of business processes.
This blog summarizes the key highlights of this webinar. The full webinar recording and presentation are also publicly available.
SAP AI Business Services
The SAP AI Business Services are a portfolio of reusable microservices on the SAP Business Technology Platform (BTP). They provide strategic machine learning capabilities that help automate and optimize processes while enriching the customer experience. The services solve a certain business problem that can be part of many different business processes with machine learning models.
The portfolio of the SAP AI Business Services includes the following services:
- Business Entity Recognition locates and identifies named entities in unstructured text.
- Document Information Extraction automatically extracts information, such has header fields and line items, from unstructured documents.
- Document Classification classifies unstructured documents as a basis for further automatic processing.
- Data Attribute Recommendation automates creation, maintenance, and management of structured data.
- Invoice Object Recommendation recommends general ledger accounts and cost centers for invoices without order reference.
- Service Ticket Intelligence classifies tickets and recommends similar tickets for faster solution proposal.
In the following paragraph I want to describe two example use cases which are using SAP AI Business Services. However, these are just examples and the services can be used in many other use cases. Also, if you want to see a few more use cases, look at the session recording of the SAP Community Call where some more use cases have been presented.
The use cases presented below outline that it especially makes sense to combine the SAP AI Business Services with each other as well as with other Intelligent Technologies, such as SAP Conversational AI and SAP Intelligent RPA.
Request Handling in Shared Service Centers
The provisioning of certain services is bundled within shared service centers to achieve higher service levels and efficiency. Typical examples of shared service centers are HR shared services or financial shared services. The shared service centers typically receive large amounts of incoming requests resulting in queued inboxes with high workloads for the shared service center employees.
In this example, let us imagine, we are having a financial shared service center, receiving, processing, and answering to supplier requests.
Suppliers send different kinds of requests to the shared service center, for example a supplier could ask for the status of an invoice that he has sent to the organization but not received the payment yet. In that case the supplier might state the invoice number of the respective invoice within his request.
When requests are received by the shared service center the SAP AI Business Service – Service Ticket Intelligence would first categorize the requests. In our example the correct category could be “Supplier Invoice Inquiry”. After that the service Business Entity Recognition would extract the relevant entities from the email, such as the invoice number. In the end an RPA bot can automatically answer to all incoming supplier invoice requests using the extracted invoice number by looking up the invoice status in the backend system and inserting the status into a predefined email template.
With this, the whole process of answering the supplier request has been performed without any human interaction and fully automated and therefore, with less errors and much faster. Many more customers can get answers in shorter timesframes.
Intelligent Invoice Processing with Account Assignment Prediction
Organizations often receive invoice without a reference document or a reference order, for example for services they have consumed. This leads to high efforts in the accounts payable departments as the accounts payable clerks have to manually search and insert the required general leger account and cost center so that the accounts payable process can be completed. This is a very time-consuming and highly repetitive task.
With the services Document Information Extraction and Invoice Object Recommendation this highly manual and labor-intensive task can be automated.
When an invoice is received by the organization Document Information Extraction would extract the header fields and line items of the received invoice. An RPA bot would then identify the invoices that were received without an order reference. The bot would then pass the invoices without order reference to Invoice Object Recommendation. This service then predicts the correct general ledger account and cost center.
With this, the process of manually having to assign cost objects to invoices without reference documents is simplified a lot.
As mentioned already in the beginning of this blog post: This was only the first community call in a whole series of community calls about the SAP AI Business Services. If you want to find our more about the single services, stay tuned for more webinars which will be announced on SAP Community Calls. You can also follow our tag SAP AI Business Services to find out about new community calls as we will publish an announcement blog post for the community calls under this tag, too.
For more information on SAP AI Business Services:
Explore: SAP Community Page
Dive deeper: Open SAP Course