Outlining SAP Business AI Integration into Cloud

Objectives

After completing this lesson, you will be able to:

  • Outline the benefits and challenges of using cloud infrastructure for AI and the differences between embedded AI and custom extensions in SAP Business applications.

AI and Cloud

AI requires significant computational power, agility for frequent updates to AI models and a flexible infrastructure to quickly activate and deploy use cases. Modern AI relies on large, pre-trained models that run on scalable cloud infrastructure like GPUs. So, AI innovation and cloud are synonymous. Furthermore, the overall experience is out-of-the-box embedded deeply into SAPs cloud solutions.  This is why SAP’s AI innovations based on generative AI will be only available in the cloud.  On-prem systems present challenges to your AI adoption. Scaling systems to meet increased AI demand and compute power is costly and time consuming. Significant resources and infrastructure are required to grow and maintain hardware and software systems. On-prem data silos and structures hinder the effectiveness of AI, limiting effective data mining.

AI requires scale, speed, and agility to achieve the computational power needed and keep up with the pace of innovation. This is difficult to achieve with traditional on-prem systems due to limits in infrastructure scaling and frequent data silos.

Customers are making this choice by themselves already today, out of the 27,000 customers that are already using SAP’s AI capabilities today, only about 1% are using narrow AI on premise.

Embedded AI vs. Custom Extensions

 Embedded AICustom Extension
DefinitionIn an embedded approach, AI functionality is seamlessly incorporated directly into the SAP Business applications.Building AI capabilities involves developing custom AI solutions tailored to the specific needs of the business using SAP BTP.
Characteristics
  • The AI features are an integral part of the SAP Business application, enhancing its capabilities without requiring separate installations.
  • Users interact with AI features within the familiar SAP environment, providing a cohesive user experience.
  • Organizations create and implement AI models and applications from scratch, leveraging SAP development tools and frameworks.
  • Offers the highest level of customization and flexibility but requires significant development resources and expertise.
Cost and ResourcesEmbedded solutions are simpler / straightforward but do have customization limitations.Building custom solutions provides maximum flexibility but demands resources and expertise.
ImplementationQuick(er) implementation cyclesDepended on IT infrastructure and SAP BTP integration

Business AI capabilities need to be directly embedded into applications and extensions. Designed with security, governance, and trust in mind, the AI Foundation on SAP BTP is our new one-stop shop for developers to do exactly that. It provides ready-to-use AI services, AI runtimes, lifecycle management, as well as tooling for generative AI capabilities and business-data connectivity. As part of the AI Foundation, our generative AI hub provides instant access to the most powerful large language models (LLM), such as Azure OpenAI Service and Falcon-40b. On the other side, it offers grounding capabilities based on enterprise data to ensure context - for example, by leveraging our new SAP HANA Cloud vector engine that combines LLMs with relevant organizational data.

Whenever off-the-shelf capabilities are available, it is almost always the most economical choice in contrast to building these capabilities yourself. In other cases, where you meet whitespaces that are not covered by SAP, it is certainly a viable option to assess the development of bespoke solutions.

By 2028, more than 50% of enterprise that have build large AI models from scratch will abandon their efforts due to costs, complexity and technical debt in deployments

According to Gartner, by 2028, more than 50% of enterprises that have built large AI models from scratch will abandon their efforts due to costs, complexity and technical debt in deployments."

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