From generative AI to enterprise level applications, creating enterprise specific AI superpowers

Author:Acloudear , 2024-07-01 19:00   
As a leading global enterprise software provider, SAP is making generative AI a trusted intelligent partner for enterprises through its commercial AI innovation solutions.


In the 1980s, artificial intelligence relied on rules, expert systems, and deterministic programming to help businesses complete tasks such as invoice matching, bookkeeping, and sales/purchase order generation. This form of artificial intelligence was called “symbolic AI”, which, although somewhat rigid, allowed everyone to taste the sweetness of technology for the first time.


Nowadays, with the rapid progress of machine learning (ML), deep learning, computer vision and other technologies, especially the deep integration with natural language processing (NLP), artificial intelligence has entered a new era – the era of generative AI.


Generative AI is reshaping our work and life with its powerful data analysis, self-learning, and real-time prediction capabilities. It can not only understand and parse natural language, but also automatically generate text, images, and even video content in the ocean of data, providing decision-makers with real-time and dynamic insights, making business process automation reach unprecedented heights.



However, making this technology truly serve enterprises and ensuring that it is both powerful and reliable is a major challenge faced by many institutions.


As a leading global enterprise software provider, SAP is making generative AI a trusted intelligent partner for enterprises through its commercial AI innovation solutions.


1.One stop AI solution: SAP business technology platform

The SAP Business Technology Platform (SAP BTP) provides SAP customers with a seamless integration of generative AI in the form of a comprehensive, multi tenant supported SaaS solution. For example, in automated customer service scenarios, BTP can analyze emails in real-time, perform sentiment analysis or proxy evaluation through LLM and LangChain frameworks (a framework for developing applications supported by LLM), intelligently classify emails, and improve the efficiency and quality of customer support.


In addition, the introduction of the core underlying SAP Build Apps on SAP BTP and the vector storage function in SAP HANA cloud further simplifies the development and deployment process of AI applications, ensuring that enterprises can flexibly apply the latest technology and achieve data-driven decision-making and optimization.


2.Intelligent Assistance: Generative AI Centers and Joule

SAP further provides developers with a toolset through its generative AI center, allowing them to easily try out various basic models, such as Microsoft Azure OpenAI and Falcon 40B. In the future, it will also include more models such as Aleph Alpha and Meta Llama 2, in order to find the most suitable AI driver solution for the core business processes of enterprises.


At the same time, SAP’s natural language generative AI intelligent co pilot Joule, designed specifically for understanding enterprise data, has been embedded in various SAP solutions such as cloud ERP, HR management, cost management, and sales management. It not only enhances the user experience, but also provides automation, natural interaction interfaces, and deeper insights, optimization, and predictive capabilities.



3.Ensure enterprise level reliability of generative AI

SAP is well aware that in order to truly integrate generative AI into the enterprise bloodline, it is not only necessary to make technological breakthroughs, but also to build a set of core principles around AI ethics, ensuring that technological development and application are always on the right track.


Guided by this philosophy, SAP has implemented a series of key control points, deeply integrating human intelligence and artificial intelligence from the design phase to ensure that the model is both efficient and reliable in supporting specific business scenarios.


Developers are assigned the responsibility of verifying model outputs and cross checking, while specialized testing teams play the role of “disruptors”, challenging cognitive boundaries before the model is put into production. Once deviations are detected, they immediately mark and recommend model fine-tuning strategies, a process known as “red team” testing.


Through commercial AI innovation, SAP not only makes generative AI accessible, but more importantly, ensures that these advanced AI applications can meet the strict requirements and expectations of enterprises.



The transformation from generative AI to enterprise level applications is not only an iteration of technology, but also a key factor in reshaping the competitiveness of enterprises. SAP is paving a solid path towards an intelligent future for enterprises in various industries by integrating a series of innovative technologies and tools, strengthening model reliability and ethical supervision.


In this journey, enterprises are not only able to achieve core business automation and improve user experience, but also able to stand out in the ever-changing market by leveraging the superpowers of AI, gaining insight into opportunities, making precise decisions, and competing fiercely in the future.


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This article "From generative AI to enterprise level applications, creating enterprise specific AI superpowers" by AcloudEAR. We focus on business applications such as cloud ERP.

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