SAP Business AI empowers AI-driven Intelligent Enterprise processes, with SAP Cloud ERP solutions and S/4HANA Cloud as central core, on hybrid multi-cloud Business Technology platforms like SAP BTP, Microsoft Azure or Amazon AWS.

SAP Business AI ✨ Best practices to unlock Cloud ERP

SAP Business AI in SAP Cloud ERP is based on guidelines and best practices to realize relevant, responsible and reliable solutions which transform business with AI-powered use-cases.

These intelligent scenarios are realized with different kinds of machine learning models from specialized models for narrowed AI scenarios to multi-modal foundation models for general purpose solutions. Foundation models can process multi-modal inputs to generate general purpose outputs for a wide range of downstream tasks.

Grounded with deep Business Knowledge, SAP Business AI offers insights for data-driven processes, recommendations to support decisions or generate new relevant content.

SAP Business AI Strategies

SAP Business AI strategies enable value proposition with short or mid-term innovations for AI-powered processes of the SAP Business Suite.

Short-term SAP Business AI scenarios are available out-of-the-box, deeply integrated into SAP Business Suite apps or can be customized with grounding techniques like SAP Joule. Narrow AI short-term solutions are designed and trained for specific tasks.

Advanced SAP Business AI scenarios, with mid-term value proposition, can be realized with ML DevOps and AI services on cloud environments like SAP Business Technology Platform (SAP BTP), Azure AI Foundry or AWS SageMaker AI.

SAP Business AI Use-Cases

AI-powered SAP Business Suite processes integrate Business AI with agentic automations into end-to-end intelligent business scenarios.

Example use-cases for SAP Business Suite domains are listed below.

• Visual inspection to detect nonconformance of defective parts in SAP Digital Manufacturing (SAP DM) and predictive maintenance of assets in SAP Intelligent Asset Management are two SCM use-cases
• Financial SAP Business AI examples are account receivable matching with the SAP Cash Application or the S//4HANA Cloud intelligent scenario late payment prediction
• HCM implements SAP Generative AI for personalized employee feedback, performance goal creation and creation of interview questions in SAP SucessFactors or job descriptions for contingent workers in SAP Fieldglass
• SAP Business AI-powered analysis of spend and market competition unlocks Spend Management with insights in SAP Ariba
• the SAP CX AI Toolkit implements AI-driven customer experience to show relevant products with personalized descriptions to customers based on recommendations and generated content delivered by SAP Business AI

Top 5 SAP Business AI Application Areas

Multi-modal machine learning models offer a wide range of SAP Business AI areas like Natural Language Processing (NLP), Intelligent Document Processing, Agents, Knowledge Mining or Computer Vision.

1. Main LLM Natural Language Processing (NLP) capabilities are sentiment analysis, key phrase identification, text summarization, entity recognition, translation or language understanding. SAP integrates NLP in various Business AI scenarios to perform tasks like the creation of structured data from unstructured text, to predict trends or patterns from learned conversational insights or to summarize information of business object pages,.
   NLP with Tokenization is widely used to create vector embeddings for Retrieval Augmented Generation (RAG) processes to augment SAP Business AI domain specific
2. Intelligent business document processing automates information extraction from unstructured documents with capabilities like translations, entity recognition or recommendations in SAP business processes. SAP Business AI document solutions or services like SAP Central Invoice Management, Azure Document Intelligence or SAP BTP Document Information Extraction (DOX) transform business documents like invoices or orders from PDF or image formats to structured data which can be used as input for SAP Business AI processes in S/4HANA Cloud.
3. Agentic applications assist as copilots like SAP Joule or perform actions autonomously in SAP Business AI scenarios to automate processes, offer insights from data, infer predictions or generate new content. Advanced agentic SAP Business AI scenarios implement Multi Context (MCP) and Agent2Agent (A2A) protocols to realize multi-agent solutions with collaboration between different domain specific agents and to enable calls to external tools and data sources.
4. Knowledge stores offer search capabilities on unstructured data types like text or images with search indices. The combination of SAP HANA Cloud Knowledge Graph and Vector Engine empower SAP Business AI solutions with contextual insights and information retrieval.
5. Computer Vision implements deep learning algorithms to detect objects with bounding boxes or to classify pixels with semantic segmentation techniques like Visual inspection in SAP Digital Manufacturing (SAP DM).

Out-of-the-Box SAP Business AI

Short-term SAP Business AI solutions are available within S/4HANA Cloud with out-of-the-box intelligent scenarios.

S/4HANA Intelligent Scenario Lifecycle Management (ISLM)

SAP S/4HANA Intelligent Scenario Lifecycle Management (SAP ISLM) offers out-of-the-box AI deeply integrated within S/4HANA Cloud business processes. These intelligent SAP Business AI scenarios are categorized into embedded scenarios, which are implemented with HANA ML libraries within the SAP S/4HANA Cloud stack, and Side-by-Side SAP Business AI scenarios provided by AI services on the SAP Business Technology Platform (SAP BTP).

S/4HANA Cloud Side-by-Side Business AI

Advanced SAP S/4HANA ISLM Business AI scenarios have to be realized on cloud environments like SAP BTP, Azure or AWS to leverage the power of cloud computing with scalable resources.

Business AI machine learning providers on SAP BTP are services like SAP AI Core, Document Information Extraction (DOX) or Data Attribute Recommendation (DAR) which are integrated into various out-of-the-box intelligent side-by-side SAP Business AI scenarios.

SAP S/4HANA Embedded Business AI

Embedded intelligent scenarios are implemented within the S/4HANA Cloud stack based on native SAP HANA Machine Learning (ML) libraries (PAL, APL) as machine learning provider to implement intelligent Business AI scenarios with low computing resource requirements. Some SAP Business AI examples are forecasting of project costs times, classifications of material groups or regression methods to predict stock movement times.

SAP HANA offers native analysis function libraries (AFL) to implement SAP Business AI within S/4HANA Cloud. Built-in functions and specialized algorithms of the Predictive Analysis Library (PAL) require knowledge of statistical methods and data mining techniques. The Automated Predictive Library (APL) automates most of the steps in applying machine learning algorithms. External machine learning framework for building models like Google TensorFlow can be integrated on-premise with the External Machine Learning Library (EML).

SAP measures the performance of classification or regression models with two indicators, Predictive Power (KI) for accuracy and Prediction Confidence (KR) to indicate the robustness of a predictive model with new data sets.