Discovering AI for SAP BTP's Sub-Solution Areas

Objectives

After completing this lesson, you will be able to:
  • Explore how AI in SAP BTP Areas
  • Explore AI in Application Development Area
  • Explore AI in Automation Area
  • Explore AI in Integration area
  • Explore AI in Data and Analytics Area

Introduction the the Lesson: Discovering AI for SAP BTP Sub-Solution Areas

This lesson contains the following topics:

  • Overview AI in SAP BTP Capabilities.
  • Explore AI in Application Development Area.
  • Explore AI in Automation Area.
  • Explore AI in Integration Area.
  • Explore AI in Data and Analytics Area.

Overview of AI in SAP BTP Capabilities

Introduction

SAP consists of multiple services and capabilities. As part of SAP's Business AI Strategy, SAP is not embedding AI into solutions like SAP S/4HANA, SAP SuccessFactors, or SAP Concur, SAP BTP's capabilities are also extended with AI features. Here, you can get a first overview of what's already there or in planning state.

AI Features of SAP BTP.

There are AI features for all SAP BTP areas. In the following content, we dive into an extract of features for the areas App Dev, Automation, Integration, and Data and Analytics. The area AI is not covered here, as inside this area there are services which are pure AI and not AI enhanced capabilities.

Note

Disclaimer: In this lesson described AI capabilities may not be available. This learning journey must not influence buying decisions. For a full overview of the feature scope, consult your SAP contact or check out the corresponding road map in the SAP Road Map Explorer

AI Overview for Application Development

Summary

Developer efficiency is a bit topic where generative AI can support. Generating code, extending code, explaining code: there are a couple of potential use cases for the future. SAP also looks into this and enriches its development tools with generative AI.

SAP Build Code is SAP's development environment optimized for JavaScript and Java, providing a complete setup for coding, testing, integrations, and application lifecycle management. It features generative AI-based code development through Joule, allowing developers to quickly build applications using AI-generated code and natural language descriptions. Next to this it will also integrate with ABAP Cloud Projects in 2025, that developers will have one central entry point for their development projects.

Along the year 2025, there are also generative AI features to expect for ABAP Cloud projects. Real-world applications include generating ABAP RESTful application programming model applications from a single prompt, completing partially written code, and providing explanations for complex code, thus reducing development time and improving productivity.

SAP Build Code

SAP Build Code is a turn-key environment for coding, testing, integrations, and application lifecycle management, optimized for JavaScript and Java. Developers can build rapidly using AI code generation with Joule and natural language descriptions. SAP Build Code belongs to the Build family and is a Pro-Code tool. With the help of generative AI features through Joule in SAP Build Code you can create:

  • Full Stack CAP Applications
  • SAP Fiori Applications
  • Mobile Applications

SAP Build Code enhances the development experience by providing intelligent, automated coding assistance. This groundbreaking tool applies advanced AI algorithms to generate code snippets, suggest optimizations, and offer real-time coding guidance, streamlining the development process, and boosting productivity for developers working within the SAP ecosystem.

Prompting a Full-Stack Application Including an SAP Fiori Front End

In the following example, you see how a prompt can create a full-stack application within SAP Build Code and its generative AI features through Joule.

Prompt

The following is a sample prompt in natural language that could be given to Joule:

Code Snippet
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Create an SAP Fiori application as per the requirements outlined in the user story below:

**User Story**

_As a contract administrator, I want to create and manage contracts and ‘parties involved’ information in the system, so that I can effectively track and handle legal agreements and ‘parties involved’ interactions._

_Contracts can cover a vast range of agreements, such as sales contracts, service agreements and employment contracts._

_Party involved: A ‘party involved’ is an individual, organization, or entity that purchases goods, products, or services from another party, typically a business or seller._

_Common attributes of a contract might include:_

1. _Contract ID: A unique identifier for the contract._
2. _Party involved Identification Number: A unique identification number for the party_ _involved._
3. _Contract Type: The type of contract (e.g. sales, service, employment)._
4. _Start Date: The date on which the contract becomes valid._
5. _End Date: The date on which the contract expires (if applicable)._
6. _Status: The current status of the contract (draft, active, expired, terminated, etc.)_

_A typical ‘party involved’ has the following attributes:_

1. _Party involved Identification Number_
2. _Party involved Name: It refers to the given name of a person or entity, or a label by which they are addressed or identified._
3. _Party involved Address: It refers to a physical location where an individual, business, or entity is situated or can be reached._
4. _Party involved Contact Information_

**_Acceptance Criteria_**

**_Scenario 1: List All Contracts_**

_Given I am logged into the contract management system, when I launch the SAP Fiori application to maintain contracts, then I should be able to view the list of all the contracts in a list without pressing the GO button. The list of all the contracts should have : Contract ID, Party involved Identification Number, Contract Type and Start Date._ _The list of filters should include Contract Type, Contract Status and Start Date._

**_Scenario 2: View Contract Details_** 

_Given I am logged into the contract management system, when I select a specific contract from the list of SAP Fiori application, then I should be able to view the contract details and parties involved information._
Submitting the Prompt

After entering the prompt, a wizard walks the developer through the remaining steps to generate a complete application.

Submit the Prompt
Result

The generated application is displayed and can be accepted or adjusted. In reality, however, it is a full-stack application that was created according to the Cloud Application Programming Model. This can now be used productively or can be extended by your professional developers.

Generative AI in ABAP Cloud

ABAP Cloud is the new cloud native approach for doing ABAP development in all ABAP environments from SAP S/4HANA Cloud (all deployment models) to SAP BTP ABAP Environment. This cloud-optimized ABAP development approach enables the creation of extensions and is useful for companies that want to move their existing ABAP applications to the cloud without having to rewrite their business logic. The programming model used by ABAP Cloud is the ABAP RESTful Programming Model, which takes REST principles into account and enables the creation of browser-based applications running either on SAP S/4HANA Cloud ABAP Environment or SAP BTP ABAP Environment.

Generative AI for ABAP Cloud has the potential to revolutionize code development by offering developers an efficient way to write code. It simplifies code structure and generation, making it easier for developers to understand and navigate intricate logic. This not only saves time but also enhances the overall quality of the code. While not released for general availability currently, some of the planned features are:

  • Efficient Code Generation: Generative AI can automatically generate code for specific tasks, reducing the need for manual coding and minimizing errors.
  • Improved Code Comprehension: The technology is able to explain to developers complex code in simple terms making it easier for developers to understand and navigate complex logic.
  • Enhanced Code Quality: Generative AI can identify and resolve coding issues, ensuring enterprise-grade quality.
  • Automated Test Classes: It can even generate test classes, reducing the time and effort required for testing.

Note

Disclaimer: Generative AI functions for ABAP Cloud are planned for 2025. For more details, check out:SAP Road Map Explorer

To learn more about the recent planned innovations for ABAP Cloud, check out this video: SAP TechEd 2024 | ABAP Highlights

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AI Overview for Automation

Summary

SAP Build Process Automation incorporates AI through its workflow features and its RPA features. Workflows are created using a visual drag-and-drop interface, facilitating their creation. An additional key feature is prebuilt content packages, which help automate business processes more quickly. RPA automates repetitive manual tasks using bots, enhancing efficiency, and reducing errors.

Introduction

The topic of automation has already been examined in detail in a previous lesson, and AI provides support in the area of SAP Build Process Automation.

Workflow - Business Process Design and Execution

SAP Build Automation: Workflow enables business users to automate workflow processes and tasks without writing code. It provides a visual, drag-and-drop interface for building workflows, allowing users to create forms, manage decision logic, and organize process flows with ease.

Sample Business Process

Key Features

  • No-code development: Build workflows without coding expertise.
  • Drag-and-drop simplicity: Create forms, decision logic, and process flows using visual tools.
  • Pre-built content packages: Jumpstart automation projects with hundreds of pre-built connectors and content packages.
  • Robotic process automation: Automate repetitive manual tasks using intelligent robotic process automation (RPA) capabilities.
  • Workflow management: Manage and monitor workflows, including process instances, and task assignments.

Benefits

  • Increased efficiency: Automate repetitive tasks and reduce manual errors.
  • Improved agility: Respond quickly to changing business needs with rapid workflow development and deployment.
  • Enhanced collaboration: Foster collaboration and visibility across teams and departments.
  • Reduced costs: Minimize development time and costs with low-code/no-code capabilities.

SAP Intelligent Robotic Process Automation - Task Automation

SAP Intelligent Robotic Process Automation is a component of SAP Build Process Automation. It enables users to automate repetitive manual tasks and processes using bots that mimic human interactions. This RPA capability is designed to simplify automation, reducing errors and increasing efficiency.

Task Automation

Key Features:

  • Visual drag-and-drop tools: Simplify automation development without coding expertise.
  • Pre-built content packages and connectors: Jump-start automation projects and save development time.
  • Robotic process automation: Automate tasks such as copy-and-paste operations, data extraction, data entry, and data creation.
  • Decision management: Implement business logic and rules to guide automation decisions.
  • Visibility dashboards: Monitor and track automation workflows and performance.

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AI Overview for Integration

Summary

SAP introduces a feature for generating integration flows to address complex integration challenges. This solution recommends standard integration content from SAP Integration Suite's 3000+ prebuilt integrations, and generate integrations based on specific scenarios, including interface mappings and test cases, to boost developer productivity. It is also planned to add Joule to the SAP Business Accelerator Hub, to navigate through the listed prepacked integration packages.

Introduction

This section focuses on the AI functions within the Integration area of SAP BTP, in particular the SAP Integration Suite. The SAP Integration Suite comprises various components, including the Cloud Integration capability. As an extract of embedded AI features within the Integration area, we're focusing on the generation of iFlows in Cloud Integration.

Integration Suite Overview

Generation of iFlows

Challenge

Customers face complex integration challenges with the diverse applications and systems in their landscape. Developers must spend a lot of time and effort to design integrations and APIs and must have the right domain and technical expertise to model transformations.

Solution
Standard Integration Content

Applying SAP Integration Suite's 3000+ prebuilt integrations to provide recommendations for required integration use cases.

Generate Integrations
  • Based on the specific integration scenarios, different connecting applications and business partners, generate interface mappings, scripts, and integration flows.
  • Potential to leverage integration data, to generate test cases and mocking back-end systems to accelerate developer productivity.
Generative AI - Generate iFlows.

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Generative AI based Integration Flow Generation

AI Overview for Data and Analytics

SAP Analytics Cloud

Summary

SAP Analytics Cloud's AI-driven capabilities include AI-powered queries for analytics in natural language and data-driven simulation. The "Just Ask" feature, available since Q1 2024, enhances natural language query capabilities for accurate and consistent analytics, increasing data literacy. Data-driven simulation, planned for late 2024 or early 2025, features Monte Carlo simulations to model business scenarios.

The following AI-driven features are planned for the SAP Analytical Cloud in the areas of Extended Planning and Analysis:

AI Powered Queries for Analytics in Natural Language

Access swiftly to insights and accelerate your use of major workflows using natural language and applying large language models.

Joule Capabilities

SAP Analytics Cloud with Joule lets planning and analytics users get work done faster and drive better business outcomes in a secure, compliant way. Users simply ask Joule questions or frame a problem in plain language. In response, Joule delivers intelligent answers drawn from a wealth of business data, while retaining context.

Just Ask

The "Just Ask" feature of SAP Analytics Cloud applies generative AI to natural language queries for search-driven analytics. This enables users to quickly access trusted insights in their preferred language regardless of expertise.

Data-Driven Simulation - Cloud Compass

This feature is planned for late 2024 or early 2025. The compass includes a Monte Carlo simulation function and identifies the most important drivers, based on which forecast and simulation values are determined that include, for example, a pessimistic, a realistic, and an optimistic scenario. No IT or statistical knowledge is required to use the compass.

This feature enables users to simulate the uncertainties in critical drivers and see the probable impact on the organization’s overall profitability even for non-technical users. The Cloud Compass provides scenario modeling capabilities that allow for comparison between assumptions, and the variances in impact highlight the sensitivity of the chosen drivers.

SAP Datasphere

Summary

The SAP Datasphere knowledge graph enables organizations to represent their real-world use of data more effectively, applying it for generative AI and improved reasoning. As data is integrated into SAP Datasphere, an ontology is automatically created to represent relationships, incorporating business context from SAP sources like SAP S/4HANA. This ontology can be extended and augmented to reflect specific organizational attributes. The resulting knowledge graph processes data as a semantic web, drawing insights from complex relationships.

Knowledge Graph of SAP Datasphere

SAP Datasphere knowledge graph enables organizations to better represent their real-world use of data, leading to more effectively applying it for generative AI and better reasoning.

As data is onboarded, transformed, and integrated in SAP Datasphere, SAP Datasphere knowledge graph automatically creates an ontology representing the relationships in the data, including the inherent business context from SAP application sources like SAP S/4HANA. This is then available to be extended and augmented through an ontology editor to reflect specific attributes within an organization. Finally, the data in SAP Datasphere is automatically applied to this ontology to create a knowledge graph that allows the data to be processed as a semantic web of relationships, drawing new insights from connections that are otherwise difficult to follow.

These knowledge graphs are better suited for answering complex, often open-ended questions. For example, a marketing manager might ask a question like: "What type of campaign for mountain bikes works best for my highest performing cities with a population over 1 million?"

Knowledge graphs can provide better context to large language models (LLMs) and prevent AI-generated "hallucinations." SAP Datasphere knowledge graph enables organizations to unleash the power of AI for even the most complex queries.

SAP HANA Cloud

Summary

SAP HANA Cloud vector engine enhances SAP HANA Cloud by using multimodel capabilities to store and compare vectors using SQL. This allows for intelligent data applications that combine human intuition with machine learning. The vector engine supports retrieval-augmented generation (RAG), recommendations, classifications, and clustering. Benefits include simplified data architecture, new insights from combined data types, and integration with open-source tools. The key features are the REAL_VECTOR data type, TO_REAL_VECTOR constructor, and similarity search functions L2Distance() and cosine_similarity().

Introduction

SAP HANA Cloud now has a vector engine, which enhances multimodel functionality, allowing customers to gain value from all relevant types of business data using a single database. The inclusion of a vector engine further enables customers to build intelligent data applications that combine the natural intuition of human interaction with powerful machine learning and multimodel processing capabilities.

Components of the SAP Vector Engine.

The vector engine in SAP HANA Cloud provides the ability to store and compare vectors using SQL. Determining similarity between vectors enables use cases such as retrieval-augmented generation (RAG), recommendations, classifications, clustering, and more. Key benefits of SAP HANA Cloud vector engine include:

  • Simplify data architecture and security with a single multi-model database with SQL interaction.
  • Gain new insights by combining spatial, graph, JSON, and relational data with vector queries.
  • Easily incorporate vector use cases in solutions based on the HANA Cloud ecosystem (clients, Python libraries, CAP, and so on).
  • Power to combine business data with graph, spatial, document, and vector data all on a single platform.
Use Case Example

In this scenario, an already trained LLM is enriched with extra data. The additional data is saved as a token in the vector engine. It is then possible to chat with this additional context and content from the text documents.

Use Case: Retrieval Augmented Generation RAG.

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