Describing SAP's AI Strategy

Objective

After completing this lesson, you will be able to describe SAP's AI offerings.

Introduction to the Lesson: Describing SAP's AI Strategy

Next up on our exploration of SAP BTP pillars is Artificial Intelligence (AI). SAP's AI capabilities are both broad and deep spanning the complete spectrum of the SAP portfolio starting with the realization of AI across the SAP Business Suite ( Joule, Joule Agents & Embedded AI) and finishing with the technical foundation of AI in SAP BTP (AI Foundation). In this first lesson we discuss AI in general and then pivot to SAP's vision for AI.

This lesson contains the following topics

Artificial Intelligence
SAP's AI Strategy

Artificial Intelligence

What is AI? A Simple Definition

At its core AI is the science of making machines smart. It's a broad field of computer science dedicated to creating systems that can perform tasks that typically require human intelligence. This includes abilities like learning from experience, understanding language, recognizing objects and sounds, solving problems, and making decisions.

What is AI? A More Detailed Look

Think about how humans operate. They take in information through their senses (sight, sound), they process that information, learn from it, remember past experiences, and use all of that to make a decision or take an action.

The goal of AI is to build machines that can do the same, often on a massive scale.

AI works by combining three key components:

Data: AI systems need a lot of data (text, images, numbers, etc.) to learn from. This data acts as the "experience" for the machine.
Algorithms: These are the "brains" of the operation. They are sets of rules and instructions that tell the machine how to process data, find patterns, and make predictions or decisions. Machine Learning (ML) is the most common type of AI algorithm today.
Computing Power: Processing vast amounts of data with complex algorithms requires powerful computer hardware (like GPUs) to do it quickly and efficiently.

Examples of AI We See Everyday

Siri, Alexa, and Google Assistant:: Understanding voice commands to perform specific tasks.
Netflix or YouTube Recommendations:: Analyzing viewing history to suggest what someone might want to watch next.
Spam Filters in your Email:: Identifying and sorting emails based on patterns associated with junk mail.
Self-Driving Cars:: Using AI for object detection, lane-keeping, and navigation.
Generative AI (like ChatGPT ):: Generating human-like text or images based on a prompt.

A Little Bit Deeper Into AI

AI is a broad field. Here are some of its most important branches:

Machine Learning: The most common approach to AI. Instead of being explicitly programmed, ML systems are trained on large datasets to find patterns and make predictions. For example, there's no need to write rules to identify a cat; you show the system millions of cat pictures, and it learns what a cat looks like.
Deep Learning: A powerful subfield of Machine Learning that uses "neural networks" with many layers (hence "deep"). It's inspired by the structure of the human brain and is behind many recent breakthroughs, like advanced image recognition and large language models (e.g., ChatGPT).
Natural Language Processing (NPL): A subbranch of Deep Learning. It focuses on enabling computers to understand, interpret, and generate human language. It's what allows you to talk to your smart speaker, translate text or have a chatbot understand your questions.
Computer Vision: This field gives machines the ability to "see" and interpret visual information from the world, like images and videos. It's used in facial recognition, self-driving cars, and medical imaging analysis.
Robotics: This branch focuses on designing and building robots. When AI is used to control the robot's actions and decisions, it's often referred to as AI-powered robotics.

A Little Bit Deeper Into Natural Language Processing

As just mentioned Deep Learning has enhanced NLP by enabling computers to focus on understanding, interpreting and generating human language with a high level of accuracy and nuance. This in turn enhances several types of tasks:

Machine Translation: Deep learning models, particularly sequence-to-sequence models, have revolutionized machine translation by learning to translate between languages with greater accuracy and fluency.
Sentiment Analysis: Deep learning models can analyze the sentiment or emotion expressed in text, providing insights into public opinion or customer feedback.
Text Generation: Deep learning models can generate human-quality text, enabling applications like chatbots, content creation, and automated summarization.
Text Summarization: Deep learning models can condense large amounts of text into concise summaries, making it easier to grasp the key information.
Question Answering: Deep learning models can be trained to answer questions based on given text or documents, providing a way to access information quickly and efficiently.

Generative AI

The ability of AI through Deep Learning and NLP to generate human-quality text leads to one of the most talked about trends in technology today, "Generative AI" (GenAI). From GenAI tools to GenAI jobs to GenAI trends the conversation keeps expanding. GenAI is so named because of its ability to create new content. While many of the most talked about capabilities of GenAI deal with the generation of text GenAI isn't just limited to text. It's capable of creating or producing many different types of content, such as images, audio, and video, all in response to user prompts This makes GenAI different than other branches of AI which focus just on analyzing and predicting. This creation capability is accomplished by GenAI models going through a "training" phase where they are exposed to vast datasets which in turn allows them to identify patterns and relationships within the data, enabling them to produce new content that mimics the style and characteristics of the training data. Going back to the "cat" analogy mentioned earlier in the lesson if a system has been shown millions of images of a cat, then it's likely capable of producing an image of a cat when prompted to do so. That's the essential essence of GenAI.

SAP's AI Strategy

AI as a Game Changer

AI is one of the most transformative technologies for businesses today. It's not just a futuristic concept; it's a practical tool that can be applied across virtually every department to drive growth, efficiency, and innovation. A few of the possibilities:

Enhance Efficiency and Automate Processes: AI excels at handling repetitive, time-consuming tasks with speed and accuracy far beyond human capability. This frees up employees to focus on more creative, strategic, and high-value work.
Improve Decision-Making with Data-Driven Insights: AI can analyze vast amounts of complex data to identify patterns, predict future trends, and provide actionable insights that humans might miss. This leads to smarter, faster, and more confident business decisions.
Boost Customer Experience and Personalization: AI enables businesses to understand their customers on an individual level and deliver hyper-personalized experiences, products, and support, leading to increased loyalty and satisfaction.
Drive Innovation and Create New Opportunities: AI can be a catalyst for creating entirely new products, services, and business models that were previously impossible.

From finance to procurement to IT and beyond there is no facet of an organization that AI cannot fundamentally transform. For SAP the value proposition of AI to our customers is simple: Through AI SAP software will fundamentally transform the way organizations operate. SAP takes a practical approach to AI believing in providing AI solutions built for organizations. In other words AI that enriches an organizations business processes and empowers their ability to steer their organization with agility. With SAP's unique expertise as a provider of business process software along with unparallelled access to business process data SAP's AI solutions can uniquely help organizations leverage their data to achieve measurable business outcomes.

Let's briefly look at two examples to illustrate the value of SAP AI solutions: SAP Transportation Management & SAP SuccessFactors.

SAP Transportation Management

Johann is a shipping clerk working in the office of Freight Overseas International Ltd. a shipping company based out of Europe. Every day ships carrying various goods will arrive at the port of Hamburg and Johann's responsibilities include looking at the shipping (sometimes referenced as delivery) notes every morning from the arriving ships and manually comparing them to the associated sales orders to make sure there are no discrepancies (i.e., what was ordered is what was shipped). While the number of shipping notes he manually reviewed daily was relatively small nevertheless it was a manual process and took about two hours time per day on average.

Using SAP Document AI one of the many capabilities of SAP Business AI changes things completely.. First SAP Document AI checks the shipping notes. It extracts all relevant information automatically from the notes and posts the data to the system.. It also performs automatically the checks Johann was performing manually, flags any errors and automatically emails Johann with the relevant information so he can resolve discrepancies much faster. By automating most of the process Johann can spend more time solving problems and making sure the trucks can keep moving.

SAP SuccessFactors

Fatima is an HR onboarding specialist. Her company assigned her to this position due to large number of new hires leaving the organization during their first year. Her main goal is to reduce this turnover. One of things Fatima does is that for each new hire she develops a custom training plan based on the manager's needs and the new hire's relevant education and experience. This effort is mostly manual accomplished via conversations with the manager and the new hire along with the posted requirements of the position and the new hire's resume. Due to the manual nature of the process it can sometimes take a few weeks to develop each new hire's custom training plan.

Using the AI features embedded in SAP SuccessFactors however this process takes only a few seconds. First the requirements of the position along with the new hire's resume are stored in the system. AI capabilities scan this information, identify gaps and using training assests (also stored in the system) generate personalized training and development paths. As with Johann in the previous example Fatima now has more time to spend interacting face to face with new hires letting AI take care of things that can be automated.

SAP Business AI

As mentioned earlier SAP's approach to Ai in centered around providing solutions to organizations that help them optimize their business processes and to steer their organization with agility. A helpful way to understand SAP AI is via the following building blocks:

Joule & Joule Agents
Embedded AI Capabilities
AI Foundation (on SAP BTP)

Let's take a look at each of these.

Joule & Joule Agents
- Joule is a copilot based on generative AI, integrated into SAP solutions for business-relevant processes like HR, finance, supply chain, purchasing, and sales on the SAP Business Technology Platform. Joule provides accurate answers when prompted, is based off natural language, can learn from existing data, and facilitates faster decision-making while ensuring data privacy.
  Imagine an SAP user prompting Joule to "Show me all outstanding receivables over 30 days past due". Joule responds with a list of those receivables. This would be an example of "Assistive AI" in which Joule responds to user requests and provides support. However "Agentic AI" is where Joule really shines. With Agentic AI users take advantage of "agents" which are more proactive and autonomous. They're capable of making decisions and taking action to achieve goals without the user intervening. Imagine instead of a user prompting Joule to generate a list of receivables they instead prompt Joule to "find all receivables over 30 days past due and in addition to decide based on each individual customers past payment history whether to generate a reminder overdue notice and if so to have it sent out via certified mail by making a pickup appointment with the lowest cost mail provider".
Embedded AI Capabilities
- Embedded AI refers to the integration of AI capabilities directly into SAP's business applications and processes. Whereas with Assistive and Agentic AI the user is involved via prompting, with Embedded AI, AI features are woven into an SAP application with no prompting by the user necessary. As an example imagine AI automatically evaluating incoming invoices for fraud detection and automatically notifying the appropriate compliance personnel. As another example imagine a "make to order" scenario where a company manufactures products according to agreed upon specs from customers. Embedded Ai could be used to automatically scan incoming production orders to ensure that they comply with the corresponding sales order specs and again alert the appropriate personnel when variances are discovered.
AI Foundation
- Naturally all of the AI functionality we've discussed so far needs a platform to power it. And also naturally it should come as no surprise that SAP BTP provides all the necessary services for AI development and usage through AI Foundation. Think of AI Foundation as a "one stop shop" that has everything organizations need.

A Quick Word on AI Ecosystem

Throughout SAP's history it has engaged in numerous cooperative and mutually beneficial relationships with other companies. AI is no exception. Partners for example leverage AI Foundation to build custom AI solutions and extensions, offering specialized expertise and accelerating AI implementation. SAP collaborates with a vast network of partners to help organizations quickly adopt and incorporate AI into their business processes.

A Quick Word on Security & Compliance

When designing products at SAP, security & compliance isn't an afterthought. It's a integral and fundamental part of product design from the ground up. As AI Foundation is built on SAP BTP, it inherits all of SAP BTP's well constructed security and compliance features including data privacy, data protection, and cybersecurity. For AI specifically the aim is to be "Relevant, Reliable and Responsible. While at first these terms may see complicated they're not. Briefly explained:

Relevant: SAP Business AI enhances users in doing what they do best while using SAP.
Reliable: SAP Business AI is grounded in organizations data and is both accurate and trustworthy.
Responsible: SAP Business AI is grounded on a robust ethical framework based on human agency and oversight.

Summary

SAP's AI strategy is centered on a single, powerful concept: "Business AI." This is not general-purpose AI for consumers, but AI that is relevant, reliable, and responsible, designed to be deeply embedded within the core business processes that SAP software manages.

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