Have you ever wished you could simply ask your enterprise data questions and receive immediate, insightful answers? The wait is over! SAP is excited to announce the upcoming integration of SAP Analytics Cloud’s analytical capabilities into Joule, SAP AI-powered conversational interface.
This integration promises to transform how business users interact with their data, making it more accessible and actionable than ever before.
What is Analytical Insights in SAP Joule?
Joule, which can be enabled in various SAP applications such as SAP SuccessFactors and SAP S/4HANA, will now feature analytical capabilities powered by SAP Analytics Cloud.
This integration allows business users to explore data and gain insights through a natural, conversational experience.
Using familiar business terms, you can simply ask a question and Joule will instantly provide answers in the form of charts and metrics.
This is made possible by Joule’s multi-model architecture, which builds on the strengths of the Just Ask technology in SAP Analytics Cloud.
Joule acts as a single integration layer across the enterprise, connecting with both operational and analytical systems.
Key Benefits of the Integration
Conversational Analytics
Forget about complex data analysis tools. With Joule, you can interact with your data using natural language queries. Simply ask questions using familiar terms and receive answers in charts and metrics.
Seamless Integration
Access analytical insights directly within the SAP applications you’re already using. There’s no need to switch between applications, which reduces friction and saves time. You don’t have to sign in to SAP Analytics Cloud to get analytical insights, instead, you can get them from the context of your business application.
Powered by Just Ask
The analytical functionality is powered by the “Just Ask” feature of SAP Analytics Cloud. Data models indexed by “Just Ask” are what Joule uses.
Time and Efficiency Savings
The integration can cut the steps required to get analytical insights by up to 80%. This isn’t just about saving time, it’s about empowering your team to make data-driven decisions quickly.
Contextualisation
Get context-rich insights by combining both operational and analytical data within one platform. This will enable your team to make more informed decisions, faster.
Accessibility
Get insights anywhere via Joule instead of using dashboards in SAP Analytics Cloud Enterprise license. This means specifically tailored insights, easy to digest analytical information in the context of SAP applications used by your organisation.
How does it work?
Joule works with data from models indexed by the Just Ask feature of SAP Analytics Cloud.
The administrator for Just Ask in SAP Analytics Cloud determines which models and data are available to Joule. Joule translates natural language queries into real-time insights from SAP Analytics Cloud, with a deep integration with JustAsk. You can then have a conversation with your data until it gives you the insights you require.
Important Details and Restrictions
Controlled Release: The controlled release is planned for Q1 2025. You must be a customer of both SAP Analytics Cloud and a Joule-enabled SAP cloud application, both running on the SAP BTP Cloud Foundry environment to participate.
General Availability: General availability is planned for the Q2 2025 quarterly release. This will only be available for SAP Analytics Cloud tenants running on SAP BTP, Cloud Foundry environments.
Initial Restrictions:
Only acquired (imported) data models are supported initially. Live data models will be implemented in the future.
Analytical insights are delivered as metrics and charts.
The Joule client interface is supported in U.S. English only for the controlled release.
Supported browsers are Google Chrome and Microsoft Edge.
Joule cannot yet be enabled directly in SAP Analytics Cloud in this initial release.
User Access: Users need a valid account in SAP Analytics Cloud to access the analytical capabilities.
Data Control: The data available through Joule is determined by the administrator of the Just Ask feature within SAP Analytics Cloud.
Conclusion
The integration of SAP Analytics Cloud with Joule marks a significant step towards making data more accessible and actionable. By enabling conversational analytics within familiar applications, SAP aims to empower every employee to make faster, more informed decisions.
While the initial release has some limitations, these are planned to be addressed in subsequent updates, further enhancing the capabilities of this powerful integration.
This integration has the potential to transform how business users interact with their data, moving away from traditional dashboard-based analysis to a natural language based interaction.
We know that SAP AI is no longer the distant future—it’s here, and it’s transforming how businesses operate, make decisions, and drive results. But what will 2025 hold for SAP AI and the companies that rely on it?
The story of SAP AI’s next chapter is not just one of innovation; it’s one of evolution. It’s about shifting from experimenting with AI to mastering it.
Let’s take a look at five key themes that will shape the AI landscape for SAP customers in 2025 and what they mean for the future of business.
The Rise of AI Agents: Moving from Assistants to Autonomous Agents
Today, we’re just scratching the surface of what AI agents can do. Sure, we have basic AI-driven search assistants, but 2025 is set to bring a new wave of more sophisticated multi-agent systems.
These AI agents won’t just be helping you search for documents or answering basic queries. They’ll be planning, reasoning, and collaborating—not just with SAP business users but with other systems too. Think of it like having an intelligent co-pilot by your side, capable of coordinating complex tasks across multiple systems. Fantastic isn’t?
What does this mean for businesses? It’s the potential for a future where AI is proactively responding to real-time business events—whether it’s a supply chain disruption or an unexpected surge in demand.
By 2025, AI won’t just help with tasks; it will predict and act in advance, allowing the SAP users to focus on strategy while the AI agents handle the repetitive, high-volume tasks.
Models: Quality Over Quantity
In the past, AI breakthroughs were driven by bigger models and more data. But 2025 will see a shift in focus. It’s no longer just about feeding massive amounts of data into models; it’s about giving AI the right data—data rich in context.
The truth is, context is everything. Without it, SAP AI is just guessing. But with context, AI becomes smarter, more accurate, and more useful.
The future of SAP AI will be shaped by how businesses leverage their own unique, rich data sources. The companies that win won’t necessarily be the ones with the biggest datasets, but those who can fine-tune their models to extract real value from their information.
Adoption: From Hype to Reality
By 2025, we’ll have moved past the hype of SAP AI. The real conversation will be about adoption at scale. Companies will no longer be asking, “Should we use AI?” Instead, they’ll be figuring out, “How do we use AI to solve our biggest problems?”
This shift will be driven by meaningful advancements in AI’s ability to integrate seamlessly with business processes. We’ll see AI working hand-in-hand with SAP business users, not just as a tool, but as a partner in achieving business outcomes.
AI as the New User Interface
What happens when AI becomes the primary way we interact with technology? In 2025, we’ll be closer to finding out. AI won’t just be behind the scenes, powering decisions—it’ll be front and center, changing how users interact with SAP software entirely.
Imagine a future where you don’t have to navigate a complicated interface or pull up endless dashboards to get what you need. Instead, you simply tell the system what you want, and the AI makes it happen.
In this new world, the user experience will be less about clicking buttons and more about communicating your intent. SAP AI will make systems simpler, more accessible, and ultimately more powerful.
Regulation: Navigating the New Rules of Innovation
AI is evolving faster than regulations can keep up, and 2025 will see an increasingly fragmented regulatory landscape. But rather than stifling innovation, regulation will provide an opportunity to shape the future of AI responsibly.
Businesses that succeed in the AI-driven world will be those that take ethics, safety, and responsible use seriously.
It’s not just about complying with the rules; it’s about setting the standard for what responsible AI looks like. The companies that build trust with their customers by using AI ethically will have a competitive advantage.
Looking Ahead: AI’s Impact on SAP Customers in 2025
As we look toward 2025, the future for AI is clear: deeper integration, AI agents, and new user interface. SAP AI won’t just be a tool; it’ll be a partner in driving business success. But success in this new era won’t come from doing more of the same. It’ll come from thinking differently about how we work, how we make decisions, and how we build our businesses.
As SAP customers, you’ll have the opportunity to lead this change. By embracing SAP AI in 2025, you’ll be setting the stage for a future where technology, SAP consultants and SAP business users work together to achieve more than ever before.
The question is, are you ready?
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SAP AI uses various AI methods, including generative AI models to empower organizations to automate processes, enhance decision-making, and generate insights from massive datasets.
Generative AI plays a significant role in SAP AI’s capacity to enhance customer experiences, and develop intelligent assistants like SAP Joule.
In this blog, we will dive into the core components of Generative AI and how these models operate – allows us to not only discuss the latest advancements but also implement them effectively in real-world SAP environments.
How Does Generative AI Work?
Generative AI allows machines to generate new, and original content. It could be text/images/videos. In the enterprise world there are many applications to it. A simple example is if you’re using the project module of SAP, A Gen AI feature within the software can generate a project summary based on the current status of the project by analysing the budget/timeline/expense/tasks etc from the system. Otherwise as a PM you’re required to create an executive summary yourself by looking at multiple screens and data. Its immensely improving the productivity.
Generative AI uses deep learning techniques like neural networks to understand patterns in large datasets. Lets understand what are the key methods Gen AI uses to create content:
1. Generative Adversarial Networks (GANs)
One of the most revolutionary approaches in generative AI is the Generative Adversarial Network (GAN). GANs consist of two neural networks working in opposition:
Generator: This network generates new content, like an image of a face. The goal of the generator is to create data that is indistinguishable from real data.
Discriminator: The role of the discriminator is to evaluate whether the generated content is “fake” (generated) or “real” (from real datasets). Over time, the discriminator improves at distinguishing between real and fake, forcing the generator to create more realistic data.
These networks are locked in a constant competition, resulting in highly realistic outputs over time. For example, in image generation, the generator creates images that the discriminator judges as fake or real until the generator becomes proficient at mimicking realistic images.
This adversarial process has been groundbreaking, especially in fields like computer vision, where GANs can generate photorealistic images, transform images into different styles, or even create entirely new ones.
2. Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs) are another significant deep learning model in the generative AI landscape. Unlike GANs, VAEs focus on learning a compressed representation (or latent space) of the data. The process involves:
Encoder: Compresses the input data (e.g., an image) into a smaller, encoded representation.
Latent Space: A compressed version of the input that holds the essential features of the data.
Decoder: Reconstructs the original data from the compressed representation.
What makes VAEs unique is their ability to generate new data based on the learned latent space. For instance, after learning the core features of human faces, a VAE can generate entirely new faces that share characteristics of the faces in the dataset.
VAEs are useful in generating diverse outputs, ranging from new visual content to potential designs, and are known for their ability to interpolate between examples smoothly.
Conclusion
Generative AI is transforming industries by enabling machines to generate realistic, high-quality content. Techniques like GANs and VAEs are pushing the boundaries of what AI can do, making it an essential tool for enterprises.
Let’s explore together how SAP AI and generative models can drive your business forward. Keep learning!
In our previous blogs, we explored various fundamental concepts surrounding artificial intelligence, including an introduction to AI, machine learning, deep learning, generative AI, large language models (LLMs), and prompt engineering. Each topic laid the groundwork for understanding the advanced capabilities of AI and its applications across industries.
Now let’s understand what’s SAP’s AI strategy.
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
SAP’s approach to AI is structured around its AI Strategy, which aims to infuse AI into applications and business processes through two main pillars:
Embedded AI in SAP Applications
Exposing Business-Centric AI Services via SAP BTP
Embedded AI in SAP Applications
SAP integrates AI capabilities natively within its business applications such as SAP S/4HANA, SuccessFactors, SAP Fieldglass etc. This allows customers to leverage AI without needing extensive development efforts.
For example, AI-assisted person insights in SuccessFactors helps managers identify discrepancies in employee compensation by analyzing historical data.
Similarly, the SAP Integration Suite now automates the creation of integration flows based on user-defined scenarios, streamlining the integration process.
In SAP Fieldglass, generative AI simplifies recruitment for external workers, enabling hiring managers to generate job descriptions 85% faster and project managers to deliver statements of work more efficiently.
Exposing Business-Centric AI Services
SAP is focused on offering targeted AI services through SAP Business Technology Platform (BTP) for custom scenarios. By partnering with AI leaders like OpenAI, NVIDIA, Microsoft etc SAP provides access to foundational models, enabling developers to build custom applications featuring generative AI.
Notably, SAP leverages Microsoft’s Azure OpenAI Service, allowing customers access to powerful LLMs without needing individual contracts with third-party vendors.
What is SAP Business AI?
You may have heard about SAP Business AI in multiple occasions and we want to simplify it for you. SAP Business AI encompasses a suite of AI-related solutions aimed at unifying SAP’s offerings.
You can image it as an umbrella under which all AI related solutions are included. It’s just an idea but not a single product. It includes multiple components.
Key components are:
Joule: An advanced generative AI copilot embedded throughout SAP’s cloud enterprise portfolio, delivering contextualized insights and enhancing productivity in a secure manner.
Embedded AI Capabilities: As discussed earlier these capabilities are embedded across SAP solutions, offering functionalities like intelligent document processing, personalized recommendations, and forecasting.
AI Foundation on SAP BTP: A collection of ready-to-use AI services and tools designed to accelerate AI integration into business processes.
What About SAP’s Own Foundation Models?
We’ve seen how SAP is using other foundation models like OpenAI’s to build AI solutions. But is SAP coming up with their own LLMs? the answer is Yes!
SAP is developing the SAP Foundation Model, a table-native AI model designed for predictive tasks on tabular data. Unlike LLMs, which excel in text generation, the SAP Foundation Model aims to outperform traditional methods in classification and regression tasks, thus ensuring reliable and relevant AI solutions tailored to business needs.
Conclusion
SAP Generative AI is not just about leveraging advanced technologies; it’s about embedding AI seamlessly into business processes and applications. By providing users with powerful tools and insights, SAP is committed to helping organizations unlock the full potential of AI to drive better outcomes.
Stay tuned for more insights as we continue our exploration of SAP Generative AI and its transformative impact on the business landscape.
In this blog, we’ll guide you through the process of building a full-stack application from scratch using SAP Build Code and SAP Joule’s Generative AI.
We’ll leverage SAP Joule’s Generative AI to streamline the development of data models and application logic for essential functionalities like customers, purchases, and redemptions. Whether you’re a seasoned developer or just starting out, this walkthrough will show you how these powerful tools can accelerate your development process and enhance your projects. Let’s dive in!
Before diving in, here’s what we’ll cover in this guide:
Create a full-stack project using a template.
Develop data entities and services with Joule’s Generative AI.
Generate application logic code using Joule’s Generative AI capabilities.
Add a UI to the application and perform testing.
Step 1: Logon to SAP BTP Trial. If you don’t an account and want to setup one this blog will help.
Once your trial account is ready follow these steps: Click on “Go To Your Trial Account”.
Step 2: Click on Boosters.
Step 3: Search for “Get started with SAP Build Code ” and Click on it.
Step 4: Click on Start.
And, wait for few minutes to setup the SAP Build automatically for you.
Step 5: Then, click on Navigate to Subaccount.
Step 6: Once you’re inside the subaccount click on Instances and Subscriptions.
Step 7: Click on SAP Build code.
Step 8: Then, Click on Create.
Step 9 :Choose Build an Application. Then, choose SAP Build Code.
Step 10: Next, choose Full Stack Application.
Step 11: Provide a name for the project of your choice and click Create.
Step 12: Click on Ok. It will navigate you to SAP Business Application Studio.
Step 13: Click on Joule Icon.
Step 14: Type “/” in the command section and select “cap-gen-app.“
Step 15: Then, paste the below prompt and press send.
“Design a customer loyalty program application. Define 4 data entities: Customers, Products, Purchases and Redemptions. Each customer must have the following fields: name, email, 7-digit customer number, total purchase value, total reward points, total redeemed reward points. All fields for each customer should be integer except name and email that will be stored as string. Each product should have a name, description and price. Purchases should include the following fields: purchase value, reward points. All fields in Purchases must be integer. Redemptions must have 1 field in integer: redeemed amount. Each purchase and redemption will be associated to a customer. Each purchase will be associated to a product and is called selectedProduct. “
Step 16: Click on Accept .
Step 17: The screen will get updated once the prompt is executed. Then choose Open Editor. And select Sample Data.
Step 18:Choose Customers and Value 5 and press on Add. It will create 5 sample records for customer data.
Step 19: Click on Enhance.
Step 20: Next, click on StoryBoard.
Step 21: And select Purchases > Add logic.
Step 22: Click on Add.
Step 23: Then, choose on Open Code Editor > Application Logic.
Step 24: Copy the below prompt and paste it into the text field as given below press the send button. It will send the prompt to Joule to process.
“Reward points of each purchase will be the one tenth of the purchase value. Each purchase value will be added to the total purchase value of the related customer. Each reward point will be added to the total reward points of the related customer.”
Step 25: Click on Accept.
Step 26: Then, choose StoryBoard.
Step 27: And, select Create a UI application.
Step 28: Fill in the text fields as follows:
Display name: Purchases
Description: Manage Purchases
Then, click Next.
Step 29: Choose Templete-Based. And, click Next.
Step 30: Choose List report page. Click Next.
Step 31: Choose main entity as Purchases. Click on Finish.
Repeat steps 27-31 to create additional UI apps for the Customers and the Redemptions.
Details to be filled for Customer and Redemptions is given below:
Step 32: Fantastic! We’re ready to test the app. To test the application click on Run and Debug button.
A new tab opens. Click on Customers > Go.
It will open the list of customer data. Choose any one of the customers to test the edit function.
Click on the “Edit” and change Name and Email as given below, and Save.
Great, now you are able to see the list of customers and you can edit it too.
Let’s test the Create functionality of the app. Click on the Create button.
And fill the details for a new customer as given below.
The new customer is now added successfully.
Summary
In summary, this guide has detailed how to build a full-stack application from a template using SAP Build Code, incorporating SAP Joule’s Generative AI capabilities. We utilized Joule to generate code for data entities and services related to customers, purchases, and redemptions, and also to develop the application logic. By leveraging these tools, you can streamline the development process and create robust applications efficiently. Continue exploring SAP Build Code and Joule to unlock further possibilities and enhance your development projects.
Here is a detailed demo to help you learn further!
In our previous blog, we talked about how Artificial Intelligence (AI) helps a robot find your lost toy by thinking like a human. Now, we’re going to take it a step further and explore Machine Learning (ML), which is like teaching the robot how to learn on its own. Let’s dive into it in a fun and simple way!
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
In our previous blog, we discussed how AI can help a robot find your lost toy. Now, let’s explore how we can teach the robot to recognize different types of toys.
Imagine we want the robot to be able to identify various toys — like cars, dolls, and building blocks. Here’s how we can do it:
Show and Tell: We start by showing the robot lots of pictures of different types of toys. For example, we show it many pictures of cars, dolls, and building blocks, each labeled with the toy’s name.
Teach the Robot: We need to train the robot to recognize these toys based on their size, color, shape, and other features. This is like teaching a friend what your favorite toys look like so they can help you find them.
Recognize New Toys: Once the robot has seen many pictures of each type of toy, it can start to recognize them in real life, even if they don’t look exactly like the pictures. For example, even if you have a new toy car that looks a bit different from the ones we showed, the robot should still be able to identify it as a toy car.
Practice Makes Perfect: The more pictures of toys the robot sees, the better it becomes at recognizing them. This is because it learns to pick out common features and differences between toys, helping it make better guesses.
This process of teaching the robot with lots of examples is known as Machine Learning.
In summary, Machine Learning is:
A subset of Artificial Intelligence: It’s one way of making machines smart.
Learning from Examples: It involves giving machines lots of data (like pictures of toys) so they can learn and make decisions based on that data.
So, just like you learn to recognize different toys by seeing and playing with them, robots learn to identify toys by looking at many examples. The more they see, the smarter they get!
Types of Machine Learning
Now, let’s dive into the different ways robots and computers can learn from data. Machine Learning can be categorized into three main types:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Each type of Machine Learning serves a unique purpose and uses different methods to learn from data. Let’s explore each type in detail.
Supervised Learning
Remember when we taught the robot to identify various toys by showing it lots of pictures of different types of toys, each labeled with the toy’s name? This method is known as Supervised Learning.
In Supervised Learning:
We Provide the Answers: Just like you told the robot which toy is a car, doll, or block, we provide the machine with examples that are labeled with the correct answers.
The Machine Learns: The machine uses these labeled examples to learn how to identify or predict new, unlabeled data.
Here’s how Supervised Learning works in the real world:
Email Spam Filtering: Classifying emails as spam or not spam based on features from the email content and sender.
Image Classification: Identifying objects in images, such as recognizing animals or detecting objects in self-driving cars.
Facial Recognition: Verifying individuals based on facial features, used in security systems or unlocking devices.
Financial Fraud Detection: Spotting potentially fraudulent transactions by analyzing patterns in financial data.
Speech Recognition: Converting spoken language into text, like in voice assistants such as Siri or Google Assistant.
Unsupervised Learning
Let’s think about how kids learn to group their classmates on the first day of school. They notice differences and similarities among their classmates without anyone explicitly telling them how to categorize them. This is similar to Unsupervised Learning.
In Unsupervised Learning:
No Labels Provided: The machine is given data without any labels or categories. It must find patterns and group similar items on its own.
Pattern Discovery: The machine identifies similarities and differences in the data, grouping similar items together based on the patterns it discovers.
For example:
Clustering Customer Segmentation: Businesses use unsupervised learning to group customers based on purchasing behavior, helping tailor marketing efforts.
Anomaly Detection in Cybersecurity: Identifying unusual patterns in network traffic to flag potential security threats.
Recommendation Systems: Suggesting products or content based on patterns in user behavior, like how Netflix recommends movies.
Reinforcement Learning
Imagine teaching a dog a new trick. You give the dog a treat when it performs the trick correctly and no treat when it doesn’t. Over time, the dog learns to do the trick more often to get more treats. This is similar to Reinforcement Learning.
In Reinforcement Learning:
Learning by Trial and Error: An agent (like a robot or computer program) learns to make decisions by receiving rewards for good actions and penalties for bad ones.
Interactive Learning: The agent interacts with its environment, learns from the outcomes of its actions, and adjusts its behavior to maximize rewards.
For example:
Game Playing: AlphaGo, developed by DeepMind, uses reinforcement learning to play the board game Go at a superhuman level, learning the best strategies through practice.
Self-Driving Cars: Reinforcement learning helps these cars learn how to navigate traffic and make driving decisions by simulating various driving conditions.
Algorithmic Trading: Making trading decisions based on historical and real-time market data to develop effective trading strategies.
Summary
Machine Learning is a branch of Artificial Intelligence that allows computers to learn from data and experiences, without being explicitly programmed for every task. Whether it’s recognizing toys, understanding voice commands, or helping doctors analyze medical images, Machine Learning is becoming an integral part of our everyday lives.
The term “Artificial Intelligence (AI)” was first used back in 1956, but it was mostly discussed in scientific research or shown in movies. However, with the rise of ChatGPT, AI has become a hot topic everywhere.
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
Artificial Intelligence (AI) — A Kid’s Perspective
Let’s break down AI in a simple way. Imagine you’re looking for your favorite lost toy.
Here’s what you need to do to find it:
1. Identify Your Toy: You need to know exactly what your toy looks like.
2. Recognize Similar Items: If you find other toys, you need to tell if they’re the same as your lost toy. If it is the same type, you’ll check if it’s your specific toy.
3. Plan Your Search: You should come up with a plan to find your toy. For example:
First, check your room.
If it’s not there, look in the living room where you play with your toys.
If you still don’t find it, ask your family if they’ve seen it.
4. Adapt to the Situation: If your room is messy, you might need to search carefully among the clutter.
Now, imagine someone tells you, “I saw your toy in the backyard.”
Here’s what you would do:
You know where the backyard is and how to get there.
You won’t mistake a different toy for your own.
When you see the toy, you’ll check if it’s your favorite one.
You can find your toy because you have all these smart abilities.
But what if we could give these abilities to a robot so that the next time you lose your toy, the robot could help you find it?
Imagine the robot can move around and take pictures. That’s a good start, but it needs to think and act like you do. For example:
1. Identify the Room: The robot should recognize your room even if the furniture is rearranged. 2. Spot Your Toy: The robot should be able to identify your specific toy from other toys. 3. Understand Instructions: The robot needs to understand what you say and follow your directions. 4. Plan and Adapt: The robot should be able to make a search plan and adjust based on the situation, like searching more thoroughly if the room is messy.
In short, to find your toy, the robot needs to have human-like intelligence.
So Artificial Intelligence (AI) is — creating smart, human-like abilities in robots, machines, or computers.
What is AI?
Artificial Intelligence (AI) is when machines or computers are designed to imitate how humans think and make decisions.
In simple terms, AI is about making computers capable of “thinking” like humans.
AI allows computers to understand and analyze data, and make decisions on their own without needing constant human help. These smart machines use algorithms—detailed sets of instructions—to process information and get better at tasks over time.
Everyday Uses of AI
You might be using AI every day without even realizing it! Whether it’s the smart features in your apps or the technology behind your favorite services, AI is all around us, making life more convenient.
Here are some common ways AI is used in our daily lives:
Personalized Fitness Apps
Fitness apps like MyFitnessPal or Fitbit use AI to track your exercise routines and dietary habits. They analyze your data to offer customized workout plans and nutritional advice tailored to your personal goals and progress.
Navigation and Traffic Apps
When you use apps like Google Maps or Waze, AI helps find the fastest route to your destination. These apps analyze real-time traffic data to provide updates on road conditions and suggest alternative routes to avoid delays.
Smart Home Devices
Smart thermostats like Nest and security cameras like Ring use AI to learn your preferences and adjust settings automatically. For example, a smart thermostat can learn when you’re home or away and adjust the temperature to save energy while keeping you comfortable.
Email Filtering and Organization
Email services such as Gmail use AI to filter out spam and categorize your messages. AI helps organize your inbox by sorting emails into different categories, like primary, social, and promotions, so you can easily find important messages.
Streaming Music Services
Music streaming platforms like Spotify use AI to create personalized playlists based on your listening habits. AI analyzes the songs you play and suggests new music you might enjoy, enhancing your listening experience.
Voice-to-Text Apps
When you use speech-to-text features on your phone or computer, AI converts your spoken words into written text. This technology is useful for composing messages, taking notes, or creating documents hands-free.
Online Travel Booking
Travel websites like Expedia or Booking.com use AI to recommend flights and accommodations based on your past searches and preferences. AI helps you find deals and options that match your travel needs and budget.
These examples illustrate how AI is integrated into our daily routines, making tasks easier and more efficient while often working quietly behind the scenes.
Understanding AI Types
Let’s break down the types of AI in a way that’s easy to understand. Imagine AI as different kinds of robots or programs that can do different things. Here are the main types:
1. Narrow AI
Think of Narrow AI as a robot that is really good at doing just one specific job. For example, imagine a robot that can only play chess. It’s amazing at chess but can’t do anything else, like make sandwiches or tell jokes. Most of the AI we use today is Narrow AI. For instance, Siri on your phone can help with setting reminders or telling you the weather, but it can’t do your math homework or walk your dog.
Example: A calculator is a type of Narrow AI. It’s great at doing math, but it can’t help you with writing a story or making a drawing.
2. General AI
General AI is like having a super-smart robot that can do almost anything a human can do. It can play chess, help with homework, cook dinner, and even understand jokes. Right now, we don’t have General AI. It’s more like something you might see in movies where robots are almost as smart and versatile as humans.
Example: Imagine a robot that can help you with school projects, play games, and even chat with you about your favorite TV shows. This robot would be able to learn and do many different things, just like a person.
3. Artificial Superintelligence (ASI)
This is the most advanced type of AI, which is smarter than any human in every possible way. ASI would be able to solve complex problems, invent new things, and make decisions better than anyone else. But, this kind of AI only exists in science fiction for now.
Example: Think of a robot that could solve any puzzle in the world, create incredible art, and invent new technologies that we haven’t even thought of yet. It’s like having a super-genius robot who knows everything!
Generative AI fits under Narrow AI
Generative AI is a special kind of Narrow AI that can create new things. Instead of just following rules or doing tasks, it can make new content, like writing stories, drawing pictures, or composing music. For example, a Generative AI might help you write a fun story about a dragon and a spaceship, or it could generate a new piece of music based on your favorite songs.
Example: Think of an AI that helps you design a new video game level or comes up with new ideas for a storybook. It uses what it knows to create something new and original.
Generative AI fits under Narrow AI because it specializes in creating new content and isn’t yet capable of the broad range of abilities that General AI would have.
AI vs. Human Intelligence: Key Differences
Artificial Intelligence (AI) and human intelligence each have their unique strengths and characteristics. While AI is remarkable at handling numbers and rules with remarkable speed and accuracy, human intelligence brings emotional depth, creativity, and adaptability. Think of AI as a super-efficient calculator, whereas human intelligence is like a vibrant, ever-evolving work of art!
Here’s a closer look at how AI and human intelligence differ:
How They Learn:
AI: Learns by analyzing vast amounts of data and identifying patterns. It becomes proficient in specific tasks by processing numerous examples.
Humans: Learn through direct experiences, conversations, and exploration. Our learning encompasses everything from mastering new skills to understanding the beauty of natural phenomena.
Speed of Thinking:
AI: Executes tasks at lightning speed, especially those it has been trained on. It’s highly efficient with repetitive and well-defined activities.
Humans: May take a bit more time, but we excel at tackling complex problems and applying creative thinking to find innovative solutions.
Memory Capabilities:
AI: Retains information and facts based on programming but lacks personal experiences or emotions. It recalls data without the context of personal significance.
Humans: Remember events, emotions, and personal details, like cherished memories or favorite songs. Our memories are a blend of both joyful and challenging experiences.
Emotional Understanding:
AI: Does not experience emotions. It operates based on predefined rules and patterns.
Humans: Experience a wide range of emotions, such as joy, sadness, and empathy. Our feelings significantly influence our actions and interactions with others.
Adaptability:
AI: Functions based on its training and may struggle with unfamiliar situations. It is highly efficient but limited to its programmed scope.
Humans: Are highly adaptable and resourceful. We thrive in new and unpredictable scenarios, using creativity and critical thinking to navigate challenges.
Creativity and Innovation:
AI: Creates within its defined parameters. It’s like an artist working with a specific set of tools and constraints.
Humans: Are adept at generating novel ideas and solutions. Our creativity allows us to invent new concepts and push beyond existing boundaries.
Understanding Context:
AI: Knows what it has been taught but might struggle with nuanced contexts, such as interpreting humor or cultural subtleties.
Humans: Have a deep understanding of complex social interactions, humor, and cultural nuances. Our brains integrate diverse types of information to make sense of the world.
Decision-Making:
AI: Makes decisions based on data and programming. It follows logical rules and algorithms.
Humans: Use a combination of logic, emotions, and personal values to make decisions. Our choices reflect our unique perspectives and experiences.
In summary, AI and human intelligence each excel in their own ways. AI is a powerhouse of speed and precision for specific tasks, while human intelligence is enriched with creativity, emotional depth, and adaptability.
Summary
In this blog, we’ve explored the fundamental concepts of Artificial Intelligence (AI), starting from its basic definition to its real-world applications. We’ve simplified AI by comparing it to a robot that helps you find a lost toy, illustrating how AI mimics human-like problem-solving abilities. We discussed various everyday uses of AI, such as in fitness apps, navigation tools, and smart home devices, demonstrating its pervasive impact on our daily lives.
We also categorized different types of AI, including Narrow AI, which excels at specific tasks, General AI, which remains a theoretical concept with broader capabilities, and Artificial Superintelligence (ASI), a futuristic idea of AI surpassing human intelligence. Lastly, we introduced Generative AI, a type of Narrow AI that creates new content, showcasing its innovative potential.
This foundational understanding sets the stage for our upcoming discussions, where we’ll delve deeper into machine learning, deep learning, and the specifics of Generative AI. Stay tuned as we continue to break down these exciting topics in the next parts of our series.
In this blog, we’ll explore how to get started with SAP’s GenAI framework. It involves setting up of SAP AI Core and SAP AI Launchpad, and we will test the foundation model based on ChatGPT.
Let’s dive into setting up SAP AI Core and AI Launchpad
Step 1: Provision SAP AI Core in your SAP BTP Global Account.
We’ve a free tier account(not the trial). A free tier account is needed to use SAP AI Core.
Logon to SAP BTP Account and navigate to subaccount. My subaccount name is Zequance AI Trial.
Under manage assignments click on edit to add a service plan.
Then click on Add service plans.
Search “sap ai” and choose SAP AI Core. Then, choose Free Tier and Extended plan.
Click on Add 2 service plan.
Note: A free-tier account is required. SAP AI Core will not be available under plans if you’re using a 90-day trial account. Additionally, remember to save your changes after adding services.
Now under entitlements you will be able to see SAP AI Core as shown below.
Step 2: Select Boosters from the navigation pane on the left, then choose the booster for SAP AI Core.
Click on Start.
Once Checking is done, click on Next.
If you have a subaccount already, choose select subaccount.
Click on Next.
Click on Finish.
Step 3: The next step is to create keys. In the SAP BTP Cockpit, go to the Subaccounts section, select Services from the left-hand menu, and then click on Instances and Subscriptions.
To create the keys needed to access your instance, click the three dots and select Create Service Key.
Enter a service key name, for example “Booster” and click on Create.
Once your keys have been created, you can view or download them anytime by locating the key, clicking the three dots, and selecting the desired option from the available choices.
Step 4: Next, run the booster for SAP AI Launchpad. Select Boosters from the navigation pane, then choose the booster for SAP AI Launchpad from the list.
Click start.
Once checking is completed , click on Next.
Choose select subaccount and click on Next.
Click on Next.
Click on Finish.
Step 5: Click on Instances and Subscriptions from Subaccounts.
Next, click on Go to Application. It will open the SAP AI Launchpad.
Step 6: Next configure AI API Connnection.
From the AI Launchpad screen click on Add.
We need to copy and paste the keys that we have created earlier in the step 3 to AI API Connection in Launchpad as given below. Then click on Create.
Step 7: Checking for foundation-models scenario.
Expand ML Operations in left pane and click scenarios.
There you will find foundation-models.
Step 8: Creating a configuration.
Navigate to ML Operations and then select Configurations.
Provide a name for the configuration.
Select the foundation-models scenario.
Choose the version.
Select the Executable ID.
Then click Next.
In Input Parameters, give name and version of the model you want to use. We’re using GPT models. Then, click Next.
Next, click Create.
Step 9: Create a deployment. To make LLM available for use we’ve to create a virtual LLM deployment.
Once the configuration is complete, click on Create Deployment.
Choose Standard duration, and click on Review.
Click on Create.
After creating the deployment, wait until its status is updated to Running.
Step 10: Testing the models using Prompt Management functionality.
Navigate to Generative AI Hub>Prompt Management.
Now we cant Test the models in different approaches.
Text Summarization:
This example tasks the LLM to summarise a given text. We’re provided it with a long text and got a summary in 30 words as requested.
Question Answering:
In this test. we’ve asked the difference between SAP AI Core and the AI Launchpad. The answers we’ve got a perfecto!
Sentiment Analysis:
And finally we wanted to check if the LLM can identify sentiment of a text.
The LLM was asked to assess the sentiment for the below text, and it was successful in identifying the “positive” sentiment mentioned in the text.
Congrats! you’ve now successfully built an SAP Foundation Model and tested it using the AI tools SAP AI Core and SAP AI Launchpad.
You can also view this tutorial in action on our youtube channel.
In today’s digital landscape, leveraging SAP’s Business Technology Platform (BTP) can provide your business with powerful tools for Artificial Intelligence. To get started, you’ll need to create a trial account, which allows you to explore its capabilities without any financial commitment.
Here’s a step-by-step guide on how to set up your SAP BTP trial account.
Step 1: Go to www.sap.com and click on the profile icon.
Step 2: If you do not have SAP universal ID register a new account by clicking Create your SAP account.
Then a popup window appears. Enter your details and click Submit button.
Step 3: Next, you will receive an email from SAP asking you to verify your email address. Verify your email by clicking Click to activate your account.
SAP AI Core is a platform offered by SAP designed to support the development, deployment, and management of artificial intelligence (AI) and machine learning (ML) solutions within the SAP ecosystem. It provides a range of tools and services that help businesses integrate AI capabilities into their operations.
In this tutorial you will learn how to setup SAP AI Core and SAP AI Launchpad.
If you’re new to SAP BTP, learn how to get your trial account in this blog.
What is SAP AI Launchpad ?
SAP AI Launchpad is designed to be a user-friendly interface that simplifies the deployment and management of AI models for business users. It provides a more accessible way to interact with AI solutions without requiring deep technical knowledge.
Lets get started with the setup of SAP AI Core and Launchpad.
Step 1: Provision SAP AI Core in your SAP BTP Global Account. I’ve a free tier account(not the trial). A free tier account is needed to try SAP AI Core.
Logon to BTP Cockpit:
Navigate to subaccount. My subaccount name is Zequance AI Trial.
Click on Entitlements on the left pane.
Under manager assignments click on edit to add a service plan.
Then click on Add service plans.
Search “sap ai” and choose SAP AI Core. Then, click on Add 1 service plan.
Note: You need to have a free tier account. If you’re in a 90 day trial account you will not find SAP AI Core under plans.
Now under entitlements you will be able to see SAP AI Core.
Step 2: Choose Boosters from the navigation pane on the left. And choose the booster for SAP AI Core.
Click on start.
Keep clicking Next until you reach Finish.
Step 3: The next step is to create Keys. In the SAP BTP Cockpit, navigate to the subaccount section, select Services from the left-hand menu, and then click on Instances and Subscriptions.
To create the keys needed to access your instance, click the three dots and select Create Service Key.
Enter a service key name and click on Create.
After your keys have been created, you can view or download them anytime by locating the key, clicking the three dots, and selecting from the available options.
step 4: Provision SAP AI Launchpad in your global account
Configure your entitlement as before, but select SAP AI Launchpad
Step 5: Next, run the booster for SAP AI Launchpad. Select “Boosters” from the navigation pane, then choose the booster for SAP AI Launchpad from the list.
Click start.
Next, click on Go to Application.
Congrats, you’ve successfully setup SAP AI Core and AI Launchpad. In the next blog we will learn how to operate with SAP AI Core.
The integration of SAP Artificial Intelligence (AI) into various business operations is transforming how companies approach efficiency, accuracy, and strategic insights. From finance to supply chain management, procurement, human resources, sales, marketing, and industry-specific processes, SAP AI is proving to be an invaluable asset.
Finance with SAP AI
SAP AI is revolutionising financial processes by automating tasks such as invoice matching, auditing, payments, and expense management.
This leads to reduced days sales outstanding and simplifies error resolution through intelligent guidance. SAP AI also helps to prevent financial fraud by detecting anomalies that might be missed by humans.
Let’s have a look at some of the real world examples:
Intelligent Invoice Matching:
SAP Cash Application uses machine learning to automate the matching of incoming payments to invoices, which reduces manual effort, lowers days sales outstanding, and enhances the quality of work related to accounts receivable.
It can automatically learn from accountants’ actions to improve matching accuracy.
Predicting Late Payments:
AI analyses accounts receivable data to predict late payments, allowing for more effective collection activities. This reduces collection costs and minimises write-offs.
Enhanced Financial Close:
SAP AI improves the goods receipt/invoice receipt (GR/IR) account reconciliation processes, leading to reduced processing times during financial close. It can also help resolve common errors during the financial close using a step-by-step guide.
Tax Compliance: AI streamlines and automates tax compliance checks, learning from manual decisions to increase automation rates over time.
Data Analysis: AI tools can rapidly analyse large volumes of data, detect discrepancies and identify trends.
Planning and Strategy:
AI supports planning and strategy by making forecasts and automating budgeting processes with predictive analytics.
AI enriches planning models with historical data from source systems and external market conditions, benefitting from predictive scenarios.
Decision-Making:
AI enhances decision-making in finance, with tools available to provide data-informed recommendations. With the ‘just ask’ feature, users can search data using natural language and get answers in the form of charts and tables.
Supply Chain with SAP AI
In supply chain management, SAP AI facilitates a more responsive approach to customer demand and production adjustments.
Here are some of the popular use cases from SAP AI:
Demand Forecasting:
SAP AI can predict customer demand using real-time information and historical data. This helps reduce inventory costs and maximize resource utilisation.
SAP Integrated Business Planning uses machine learning and statistical models to generate accurate demand plans.
Quality Control:
AI-enabled visual inspections and anomaly detection improve quality assurance and help to mitigate risks before they escalate. Machine learning can be used for visual inspections, enhancing production efficiency with automated planning and dispatching.
Predictive Maintenance:
SAP AI can predict equipment failure using IoT data and send warnings to maintenance and operations. This enables proactive maintenance, reduces downtime, and uses machine learning to analyse failure modes.
Optimised Logistics:
AI automatically derives slotting rules from product data in warehouses and generates statistical models from system data. It can also automate the processing of goods receipts and delivery notes, improving operational efficiency and data quality.
Product Development:
AI enhances product development by connecting 2D or 3D product models to business information and process-related data. It helps to reduce time spent on tagging master data to 3D visualisation objects by automatically identifying and assigning master data to components.
Risk Management:
AI helps evaluate supply chain risks and projected delays, helping businesses focus on the most critical shipments. It can also improve demand forecast accuracy by considering multiple factors such as inventory levels and weather changes.
Procurement with SAP AI
SAP AI transforms procurement into a proactive, data-driven operation. Here are a couple of examples:
Automated Compliance: Automated systems provide on-screen recommendations to ensure purchasing compliance.
Data Extraction: AI streamlines data extraction and minimises manual effort, reducing errors and improving decision-making. It provides prescriptive guidance based on historical data and trends.
Human Resources with SAP AI
AI in human resources fosters a dynamic and inclusive workforce environment. Lets find out more use cases in HCM.
Talent Acquisition:
AI enhances talent acquisition by matching candidates with suitable job opportunities and improves the employee experience using digital assistants for real-time help.
SAP AI can also quickly generate job descriptions and interview questions, identify qualified candidates, and speed up the hiring process.
Employee Engagement and Retention:
By personalising experiences and providing tailored career growth recommendations, SAP AI creates a more positive and productive work environment, increasing employee satisfaction.
Efficiency:
SAP AI streamlines HR processes, such as compensation discussions, candidate assessment, and job description creation, by up to 90%. It allows HR to focus more on strategy and less on routine tasks.
Sales and Marketing with SAP AI
SAP can personalise customer interactions with AI by tailoring product recommendations and enhancing lead scoring to strengthen sales pipelines.
Personalised Experiences:
SAP AI helps businesses understand and respond to customer behaviour, minimise churn, and maximise upselling opportunities. AI marketing automation can target customers based on lifecycle stage, engagement propensity, or estimated spend.
Product Recommendations:
SAP AI recommends products based on customer interactions, purchase history and browsing patterns. SAP Intelligent Product Recommendation uses historical data and machine learning to simplify price quotations and recommend products based on customer needs.
AI-Driven Content:
Generative AI can be used to tailor email subject lines and preview text, as well as enrich product descriptions and tags.
Audience Segmentation:
Its key to segment audience to reach out to them with more personalised messages. SAP AI is enabling faster creation of audience segments using natural language and generative AI.
Sales Efficiency:
AI identifies high-potential leads and guides sales teams, optimising sales efforts and customer engagement strategies.
SAP AI can also generate tailored information to enhance the sales team’s understanding of accounts and leads, as well as recommend next actions and email drafts.
Shopping Assistance:
It might to new for you but, SAP AI shopping assistants use natural language to help customers find products online.
Industry-Specific SAP AI Applications
Industry-Specific SAP AI Applications
AI is versatile, and it’s proving its value across industries by simplifying complex processes and modernizing operations.
Agriculture:
Farmers can make better growing decisions with SAP AI. It analyzes weather patterns, soil conditions, and market trends, offering insights that promote sustainable farming. With SAP Intelligent Agriculture, the plan-to-harvest process becomes more efficient.
E-Mobility:
Managing electric vehicle fleets is easier with AI. It identifies the best charging times to lower costs and prevent overloading the power grid. SAP E-Mobility helps businesses manage their EV charging networks smoothly.
Retail:
Retailers are turning to AI for data-driven decisions. From predicting demand to automating processes, SAP AI personalizes product recommendations and optimizes merchandising. It ensures customers see the products that matter most to them.
Manufacturing:
SAP AI takes the guesswork out of production. It improves efficiency through better planning and automates dispatching, allowing manufacturers to streamline their operations.
Trade Promotion:
SAP is making trade promotions more effective. It uses AI forecasting models to predict the outcomes of promotional plans, helping businesses optimize their spending. SAP Revenue Growth Management ensures that trade promotions are planned and executed efficiently.
Looking Ahead
The application of SAP AI across these critical sectors underscores its transformative potential in business.
By automating complex processes, providing insightful data analysis, and enhancing decision-making capabilities, SAP AI is not just a tool for innovation—it is a foundational element that propels businesses towards a more efficient, accurate, and insightful future.
As industries continue to embrace SAP’s AI solutions, the scope for growth and improvement expands, paving the way for a new era of business operations that are smarter, more responsive, and continuously evolving.
SAP will continue to reshape human roles and responsibilities with AI, creating new tasks and shifting the focus of work toward strategy and analysis.
SAP’s approach to AI goes beyond the usual. SAP said it will spend 2 billion euros on AI, which is huge. SAP doesn’t see AI just as an add-on but as a key part of its business software, smoothly built into it.
This integration of GenAI technology with Business Applications is more than just an improvement; it marks a major change towards smarter business processes, ready to change the way companies work, make decisions, and enhance their performance.
The Dawn of Generative AI in Business
SAP’s foray into generative AI is driven by a vision to deliver unmatched value to its customers. By contextualizing generative AI within business applications, SAP aims to unlock the potential that was once deemed unattainable. The focus is on creating tangible benefits that resonate across various industries.
In a series of recent announcements, SAP has outlined its roadmap for generative AI and its commitment to innovation. The launch of a generative AI hub at the end of 2023, and the introduction of the SAP HANA Cloud Vector Engine, expected by March 2024, are testament to SAP’s strategic investment in AI technology.
Joule: The Next-Generation Digital Assistant
A standout innovation in SAP’s AI portfolio is Joule, it was launched in Sep 2023, a generative AI-powered digital assistant. Joule represents a leap forward in how businesses interact with AI, offering personalized assistance for daily tasks.
Joule is embedded with SAP’s enterprise cloud portfolio. It is not merely an addition to the existing tools but a transformative natural-language, generative AI copilot designed to revolutionize the efficiency and effectiveness of businesses across various sizes and sectors. This innovation promises to integrate seamlessly into daily operations, leveraging the power of artificial intelligence.
SAP’s Generative AI Architecture: A Blueprint for Innovation
At the heart of SAP’s generative AI strategy lies a deep understanding of business processes, which guides the integration of AI across its application portfolio.
The architecture below shows how Generative AI is placed through SAP’s Cloud portfolio. It comes with the tools and libraries that you need to build and customize the solution.
SAP is developing a wide range of use cases from question answering, text generation, classification, and exploring the potential of emerging paradigms such as LLM agents.
Looking Ahead: SAP’s Vision for the Future
SAP’s vision for the future of generative AI in business applications is both ambitious and forward-thinking. The company plans to continue enhancing its platform, integrating generative AI in innovative ways that go beyond current applications. This includes the development of writing assistants, intelligent process recommendations, and other AI-driven tools designed to empower businesses. SAP’s roadmap for generative AI reflects a commitment to staying at the forefront of technology, driving progress, and delivering value in the ever-evolving digital landscape.
In conclusion, SAP’s integrated approach to generative AI is not just a strategic move; it’s a vision for the future of business technology. By embedding AI into the fabric of business applications, SAP is setting a new standard for intelligence, efficiency, and innovation. As companies navigate the complexities of digital transformation, SAP’s generative AI solutions stand as a beacon of possibility, offering a glimpse into a future where businesses are smarter, more agile, and more connected than ever before.
