In the previous unit you learned how we fit in SAP's strategy to deliver the Intelligent Enterprise.
In this unit, we will talk about intelligent SAP applications.
The idea behind having intelligent applications is to learn from human capabilities to support humans.
What it means is, for example humans have cognitive abilities
to learn and perform different tasks. For a machine, this can be replaced by machine learning
algorithms where it can learn from sample datasets and apply that knowledge for future decision making
such as predictions, recommendations, clustering, and so on. We can also mimic the human's listening
and speaking capabilities by using the bots and having artificial intelligence-based conversations.
Intelligent Robotic Process Automation can perform humans' repetitive acts.
And at the heart of it all, Intelligent applications is the core onto which all other
services can develop and integrate, and without them, it's not possible to perform other functionalities.
In this architectural view, you can see that SAP Data Intelligence
is based on the SAP Cloud Platform. Actually, SAP Data Intelligence
is available in two different flavors, on-premise and in the cloud.
Since our goal is to have feature parity between the on-premise version and the cloud version,
we will focus in this openSAP course on the cloud version, because it is the offer which was available first.
Like we mentioned, SAP Data Intelligence is the foundation for building intelligent applications.
We have SAP Leonardo AI Business Services that can be used by standalone machine learning business applications
or can be integrated in the Intelligent Enterprise suite, as you see on the right-hand side.
In the previous unit you saw an example of how SAP Cash Application was embedded in S/4HANA
to automate invoice-payment matching. In this unit we will show you more examples
of embedding SAP Leonardo AI Business Services in the Intelligent Enterprise suite.
SAP Leonardo Machine Learning applications are grouped into three categories.
First, the SAP Leonardo AI Business Services which provide certain business-relevant functionalities
and are built on SAP machine learning technology. We will show you an example
in this unit, which is Document Information Extraction. Second, the embedded ML business applications,
like the Learning Recommender, which we will talk about later.
And last but not least, the standalone applications, like Cash Application,
which we discussed in the previous unit. Let's talk about the Service Ticket Intelligence.
Large enterprises provide different channels like email, paper mail, phone calls,
and chatbots to their customers, and get a lot of correspondence
and service requests via the different channels. Usually they use a CRM tool
to manage all those different requests. The requests or tickets have to be classified,
so that they can be routed to the right team for processing to get solved or answered.
In principle, the classification can be done manually, but you can also automate this.
The way to do it is to extract the service tickets of the last one to two years out of your system,
including the ticket classification, prioritization, and solution.
This information can then be used as labeled data. You can then train your system on recognizing
and predicting the correct category or priority for these tickets.
This improvement can help with routing to the suitable team and also facilitate the solution
recommendation for the ticket, based on the solutions for similar tickets in the past.
They are sorted in descending order and can speed up the ticket solving process.
As you can see, by adding intelligence to your service ticket,
you not only speed up your service response times but also have higher rates of ticket closure,
and at the end, increase customer satisfaction. We have used a similar classification method
to help an SAP customer, CC Energie, to solve their problem with the incoming emails.
They receive about 120,000 incoming emails per year. It's not efficient to manage all these emails manually.
So in order to improve the process, they used AI and machine learning to run easier classification
with language and keyword identification, and therefore improve customer communication and satisfaction.
Let's look at a demo. As you can see here, for instance we have a customer
that sends a message via email. Then the company will receive their email
and the content in this message is analyzed using machine learning.
By that, they can find the category of the email, what it's actually talking about,
they can assign it automatically to the specialized team, and also they can use the recommendations
to help the agent answer the inquiries. As you can see, using machine learning
can make it easier to solve the problem and increase the customer satisfaction.
Another example is Learning Recommendations. Unfortunately, the good old learning catalogs
are not as useful anymore. What if we can use machine learning
and AI to automatically create communities of similar learnings, or create similar groups
of similar learning contents based on topic, type, level, and so on,
and propose those to the learners. And that is what we did.
We used multi-dimensional datasets such as employee job profile, their learning history,
their topic preferences, and course information to turn every employee into an active learner,
whereby our customers who use SuccessFactors can benefit from this embedded service.
Another example of the embedded intelligence is what we call Concur Invoice.
Probably you know Concur Travel and Expense? And you already use the mobile app where you can just take
a picture of your hotel or taxi bill, and the line item information is extracted automatically?
Concur Invoice is similar, but it works on invoices. This service has resulted in more than 80%
in cost savings per processed invoice through ML and is heavily used by more than 3,500 customers
with more than 1.2 million invoices per month. As you know, invoices can be available in electronic
or in paper form. If in paper form, first our Optical Character Recognition,
OCR, system has to process the document. OCR is one of SAP Data Intelligence's functional services
that is available out of the box. We look at various data sources, such as master data
and transactional data for correct matching, and also the business document for validation.
We then use the OCR to digitize the document and then match the information.
We also have another service called Document Information Extraction, which has its own OCR service,
and uses a deep-learning model which uses not only the textual information from the document,
but also the two-dimensional location of the text on the document as input for extracting the information
from the document. Let's look at a demo.
As you can see here, we can upload our invoice as a PDF document, and then the system will go through
this document, scan it, and extract important information, such as the invoice date or, for instance,
the purchase order number, the subtotal, the total gross, the vendor's address, the vendor name.
And after that, it can actually create a line item for the document, which collects
the most important information, such as the description of it,
or, you know, the quantity and the total gross, and then we can use this information
as part of our business application. Perfect, hope you enjoyed this unit.
In this unit we learned more about some built-in ML and AI capabilities of SAP Data Intelligence,
which are embedded in some business applications, such as Invoice Reader embedded in Concur,
Learning Recommender embedded in SuccessFactors, or Service Ticket Intelligence integrated
with the SAP Service Cloud. In the next unit, we will talk about some
SAP Data Intelligence use cases and our customer success stories.
Good luck with your self-assessment and see you in the next unit.