SAP Business Technology Platform & Artificial Intelligence

The concept of artificial intelligence (AI) is getting increasingly important, but is often still abstract. 

Companies often struggle to define specific goals and expectations for the use of AI. 

AI covers a broad spectrum of technologies and applications, which makes it difficult to find the right approach. 

Many companies do not have the necessary expertise or resources to plan and implement AI projects effectively. 

Going through an innovation process from the idea to the functional AI prototype

Overview of the SAP AI components and their possible applications 

Interactive session using concrete examples and design thinking methods​ 

Transfer of methods for the optimal collection and concretization of ideas

Critical evaluation of ideas (which is of fundamental importance in the field of AI) 

Existing AI functionalities become known and can be put to good use. 

Practical approach where participants learn methods and benefit from lessons learned and apply them to create a tangible AI prototype. 

Practical and relevant session covering real-world use cases and their critical evaluation. 

Creation of future viability and scalability 

Participants can take their generative AI prototype with them. 

Implementation of complex AI use cases in the analysis area 

Integration of the component used without manual data transfer 

Complex data preparation and quality assurance 

Sustainable operation of AI extensions 

Lack of resources due to special AI expertise in connection with SAP BTP 

Complexity of the BTP architecture with a wide range of software services 

Carrying out a BTP review for the inventory

Definition of the use case 

Conception of data lakes/data sources and interface analysis 

Review of AI methods and selection of a method 

Realization/programming of the minimal viable product (MVP) 

Cost estimate for installation in production 

Customized AI use case targeting specific business requirements ​

First successful and usable AI implementation as a proof of concept 

Integration of the AI models into the SAP Analytics Cloud 

Improvement of analysis processes in reporting and planning through intelligent automation 

Driving innovation in the company 

Better understanding of requirements through close collaboration 

Use cases exist, but implementation is unclear 

Sustainable operation of AI extensions 

Lack of resources due to special AI expertise in connection with SAP BTP 

Complexity of the BTP architecture with a wide range of software services 

Critical evaluation of ideas including feasibility assessment

Overview of SAP AI components and solutions 

SAP and Scheer best practices including reference projects 

Implementation of a BTP review for the inventory 

Architectural propsal 

Initial planning of the implementation 

Cost estimate for the implementation 

Estimate of operating costs incl. licenses