Release of ChatGPT by OpenAI has changed everyone’s perception about AI and especially Generative AI. Generative AI is disrupting the entire world and SAP is no exception. It has dramatically changed SAP ecosystem, how business run, how decisions are made and how processes are optimized. From last couple of years, SAP is investing aggressively in AI, embedding generative AI capabilities into various SAP solutions as well as SAP BTP.
In this blog series, we will learn:
- How SAP is leveraging AI and generative AI across it’s portfolio.
- What SAP Business AI is and how SAP partners and customers can use it?
- What is SAP AI Core? What is SAP AI launchpad? What is Joule? etc.
- What role SAP BTP plays in SAP's AI strategy? ... and many more.
To make it easy to understand, the blog series is divided in small parts. Each blog requires maximum 10–15 minutes to learn.
- Part 1 – Introduction to SAP Business AI [Current blog]
- Part 2 – Components of SAP Business AI
- Part 3 – Joule, SAP’s Generative AI Copilot
- Part 4 – AI Foundation on SAP BTP
- Part 5 – Introduction to SAP AI Core
- Part 6 – Introduction to SAP AI launchpad
- Part 7 – Get Started with SAP AI Core and SAP AI Launchpad
- part 8 - Hello World using SAP AI Core
- Part 9 – Overview of Generative AI Hub
- Part 10 – SAP HANA Cloud vector engine
- Part 11 - RAG, a real-life use case using SAP AI Core, Generative AI Hub and HANA Vector Engine
This is the 1st blog in the series, where will understand SAP’s AI strategy. We will also learn what SAP Business AI is and why it a game changer for SAP partners and customers.
Note: I will publish thesubsequent blogs soon.
Anyone who wants to learn AI capabilities and solutions provided by SAP and use that to build your own AI applications.
None.
However, if you are new to generative AI, I will recommend you to spend few minutes reading my blog series Generative AI for Beginners first.
Let’s start! Before going into SAP Business AI, it's important to understand few important concepts first.
Artificial intelligence is when machines/computersmimicthe way humans think and make decisions.
AI enables computers tothink as we human think.
In simple words —AI is when we enable computers to Think.
Machine Learning is:
- Asubset of Artificial Intelligence.
- Which enables machines (or computers) tolearn from dataandmake decisions.
Can the machine learn the way we human (human brain) learn things? — This was the idea behind innovation of Deep Learning.
Deep learning is asubset of Machine Learning(ML is again a subset of AI).At its core, deep learning is based onArtificial Neural Network (ANN),which is a computational models inspired by the structure and functioning of the human brain.
Generative AI is:
- A type of artificial intelligence
- that can create new things, for example artwork, music, or even realistic images.
- without being explicitly toldwhat to create
While traditional AI focuses on specific tasks or solving a problem, Generative AI is distinguished by its ability to exhibitcreativity similar to human creativity. Generative AI is capable of generating new, unique content, ideas, or solutions as we human do.
To know more about these basic concepts, check Generative AI for Beginners.
The AI market is primarily divided into two categories – AI Platforms and Embedded AI.
AI platforms
The AI platform market consists of general-purpose AI platforms provided by niche vendors and hyperscalers. These platforms provide generic AI functional services such as speech-to-text, text-to-image, text-to-video, text-to-audio etc. These vendors provide tools and frameworks for building, deploying, and managing AI applications as well.
Here are some prominent vendors in this space:
Google offers TensorFlow, an open-source machine learning library that facilitates the development and deployment of AI models. Google also offers pre-trained models like BERT for natural language processing tasks.
OpenAI offers ChatGPT, DALL-E (to create images from text), Sora (to create videos from text). OpenAI also offers GPT-3 and GPT-4 via API, allowing developers to integrate natural language processing capabilities into their applications.
IBM offers Watson, which provides AI tools and applications for language processing, computer vision, and data analysis.
Nvidia offers DGX platform that combines NVIDIA's software, infrastructure, and expertise to help develop AI.
Embedded AI
Let’s take an example of Siri. Siri is implemented using AI. However, customers consume this AI application natively as part of iOS. Customers don’t pay additional fee, don’t sign any additional contract or don’t develop any additional stuff. – That’s what embedded AI is.
Embedded AI Application means:
- Customers consume AI functionality natively as part of standard applications,
- They don’t develop anything, but just configure and use
- There is no separate contract, it’s part of existing license
Other well-known examples of embedded AI from non-SAP world are:
- Alexa
- Prices and arrival time prediction in Uber
- Text prediction in MS Office 365
- Product recommendations in Amazon
Customers expect SAP to solve business problems more effectively and innovatively. Therefore, instead of competing in general-purpose AI platform market, SAP is focusing on using AI to solve customer’s business problems.
SAP’s AI strategy is to use AI capabilities to empower business applications and business processes.
There are 2 main pillars of this strategy:
- Embedded AI in SAP applications
- Expose Business-centric AI services via SAP BTP
Let’s understand these 2 pillars in detail.
1. Embedded AI in SAP applications
SAP is providing embedded AI capabilities natively into SAP business applications (such as SAP S/4HANA, SuccessFactors etc.) as well as SAP BTP. These embedded AI capabilities increases the value of SAP solutions and enable customer to reap the benefit of AI without any additional contract or additional development.
With Embedded AI, IT and business customers can simply configure and use the power of AI with business context and data.
Let’s take an example to understand this better - SAP Cash Application. SAP Cash Application is a bundle of cloud microservices to automate and simplify the order-to-cash process with AI and ML technology. The application can be used inside SAP S/4HANA.
2. Expose Business-centric AI services via SAP BTP.
SAP is focusing on offering the same targeted AI services for custom extensions which are used to enable AI functionalities inside SAP’s own solutions. For this, SAP is providing several business relevant AI services for customers and partners available via SAP BTP.
Note: As we move along the blog series, above points will become more clear to you.
Now, one of the most important terms that we need to understand is – SAP Business AI. Let’s go ahead and demystify this.
All the applications, services and tools SAP is offering in AI field, are unified under one single umbrella called - SAP Business AI.
SAP Business AI is not a single product, rather it is collection of SAP’s portfolio of AI services and AI applications.
In other words, we can say that SAP Business AI is:
- Product portfolio of SAP’s AI applications and AI services
- To automate and optimize business processes and user experience
- running on and powered by SAP BTP
Below image gives a high level glimpse of SAP Business AI and it’s components.
Note: At this point, it’s ok if you do not understand this image completely. We will discuss this in detail in the next blog.
You might wonder, why the term “Business” is added.
Instead of competing with general purpose AI platform, SAP is focused on solving customer’s business problems with AI. SAP Business AI is AI built for business. It is SAP’s way forward to use AI to solve business problems and manage business process more efficiently.
SAP’s main differentiators are – it’s access to business data, understanding of the context of complex business processes, and deep domain and industry expertise.
Although, the general-purpose large language models are pre-trained on huge data and offer amazing opportunities, they also have limitations. For example, LLMs may rely on outdated training data and lack company-specific data and business process context.
This is where SAP’s strength comes into the picture. SAP has agreements with more than 25,000 customers to use their data to train our it’s own models. Using this data to contextualize generative AI models results in very task-specific outcomes for business users.
Without the business data and context, customers get results from AI model that are more like dinner-table conversation than business-altering insights and responses.
The true value of business AI comes from knowing how to apply AI to solve specific business problems. For example:
- An HR manager can use SAP AI solutions to create a job description or interview questions and do this at scale.
- A sales manager can use SAP AI solutions to automate data extraction from complex systems and generation of various reports.
- When booking a trip, employees can receive offerings that are in line with company policies just by describing their travel requirements, such as destination and dates.
- In warehouses, where delivery notes need to be matched with orders, SAP AI solutions can automate this process or provide detailed assistance to warehouse workers, reducing manual labor.
Another important differentiator about SAP Business AI is the security, compliance, ethics and trust. SAP AI solutions may not be the fastest or the cheapest, but it is surely very secure, and follows all the principals of AI ethics. AI ethics in SAP is not just a compliance part but the most important pillar which powers all AI solutions.
To summarize, we can say that - SAP Business AI helps customers to leverage AI, powered by their business data in a secure, compliant and ethical way.
If you have reached so far, don't forget to give a kudos to support the hard work 😉😉
Part 2 – Components of SAP Business AI
For more information, you may also refer to:
- https://www.sap.com/india/products/artificial-intelligence.html
- SAP Discovery Center Mission - Get Started with SAP Business AI
- Join the SAP AI Services Community to stay updated.
- SAP Business AI White Paper
Disclaimer – All the views and opinions in the blog are my own and is made in my personal capacity and that SAP shall not be responsible or liable for any of the contents published in this blog.
