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SAP Artificial Intelligence – An Introduction

Think about AI; you might picture self-driving cars, robots, or ChatGPT. But it’s crucial to peek behind the curtain and grasp how AI works and its effects for today and tomorrow.

AI has been around formally since the 1950s, defined as machines doing tasks that once needed human smarts. This broad definition evolved over years of progress.

When labelling machines as intelligent, like computers, it’s key to define “intelligence” to decide if they truly possess it.

Our unique intelligence distinguishes us from other living beings and shapes our experience. It can be defined as the capacity to adjust, solve problems, plan, adapt to new situations, and acquire new knowledge.

Given the importance of intelligence to our experience, it’s natural that we seek to replicate it artificially through scientific efforts. 

Modern AI systems such as SAP AI Assistant – Joule exhibit aspects of human intelligence, including learning, problem-solving, perception, creativity, and social skills.


Types of AI Available in SAP

AI is booming in tech, but currently available AI models are just scratching the surface with what’s known as “artificial narrow intelligence.” 

The other two types remain in the realm of science fiction for now. With the rapid pace of computer science, the future of AI is uncertain but promising.

1: ANI (Artificial Narrow Intelligence)

ANI, also called “weak” AI, represents the current state of artificial intelligence. 

While it can handle tasks driven by complex algorithms and neural networks, it’s limited in scope and purpose-driven. 

Examples of narrow AI include facial recognition, internet searches, and self-driving cars. It’s called “weak” not due to its lack of power but because it lacks the holistic intelligence associated with humans. Philosopher John Searle describes narrow AI as useful for testing hypotheses about minds but not truly possessing consciousness.

2: ASI (Artificial Superintelligence)

ASI systems possess a profound self-awareness, transcending mere mimicry or comprehension of human behaviors. With unmatched processing power, they could herald a future where humans risk redundancy.

Though such a scenario may not materialise in our lifetime, the rapid advancement of AI urges us to contemplate ethical guidelines and responsible management. 

As scientist Stephen Hawking noted, “harnessing AI’s potential while evading its pitfalls demands diligent research and consideration.”

3: AGI (Artificial General Intelligence)

AGI, or artificial general intelligence, aims to tackle any mental task humans can do. Unlike specialised AI, AGI goes beyond just learning from data; it extrapolates that knowledge to new tasks and situations.

The Summit Supercomputer showcases AGI-like capabilities by crunching through 200 quadrillion computations per second. While AGI models don’t need that extreme power, they require computational abilities currently found only in supercomputers.

Benefits of SAP AI

A few decades ago, many businesses were starting to try out AI in their operations, and its benefits were mostly theoretical. 

But as time passed, AI improved, and businesses began seeing real value in it. With AI improving all the time, businesses are finding more and more ways to use it creatively. Today, AI is bringing measurable benefits to businesses in many different ways.

1: Business-Wide Resilience

Before computers were around, businesses already saw the importance of collecting and making sense of data about their operations, customers, and the market. 

As data got bigger and trickier, it became harder to analyse quickly and accurately. AI steps in to help tackle this challenge. It doesn’t just manage big data; it turns it into useful insights. With AI, tasks can be automated, resources can be used smarter, and we can foresee and respond to changes (good or bad) more effectively.

2: Better Decision-Making and Customer Service

Great business leaders aim to make quick, well-informed decisions. When it comes to big decisions, they often involve many intricate parts and connections. 

AI boosts human knowledge and know-how by analysing data deeply and offering practical insights, aiding in confident decision-making.

It also helps businesses tailor their services and connect with customers instantly. As people progress from showing interest to making purchases, they create detailed data. 

AI empowers several business tools to use this data effectively, improving customer support and interaction. So, all in all, it’s better.

3: Product and Service-related Relevance

Traditional R&D methods used to focus on past data, often analysing performance and customer feedback only after a product was already out there. 

They could not swiftly identify market gaps and opportunities. AI changes the game by letting companies analyse diverse data sets in real-time. They can tweak existing products or create new ones based on the freshest market and customer insights.

Bonus: Getting Engaged Workforces

According to a recent Gallup survey, businesses with highly engaged staff tend to be about 21% more profitable, on average. Using AI at work can lighten the load of boring tasks, letting employees concentrate on more satisfying work. 

AI-powered HR tools can detect when employees feel stressed, fatigued, or uninterested. AI can help employees maintain a healthier balance between work and personal life by suggesting personalised wellness tips and assisting with task management.

Technologies Included in AI

To make AI truly valuable, it needs to be practical. Its real power shines when it provides useful suggestions we can act upon. 

Imagine AI as a human brain: the tech tools are like the hands, eyes, and body movements – they bring the brain’s ideas to life. 

Here are a few key AI technologies that are widely used and progressing fast.

1: Machine Learning

AI involves various technologies that allow machines to do things typically done by humans. Machine learning, a part of AI, is about algorithms learning from data to get better without being explicitly programmed. AI uses data to make decisions and predictions, while machine learning helps AI systems learn and improve by themselves as they gather more data.

2: Natural Language Processing

NLP helps computers understand written words and spoken commands. 

It can translate human language into a form computers can understand. NLG, a part of NLP, makes computers produce human-like language. 

In some advanced cases, NLP can understand feelings and attitudes from context. Chatbots and voice assistants like Siri and Alexa are common uses of NLP.

3: Robotics

Robots have been around for a while, especially in factories, but without AI, they need to be manually programmed and adjusted. 

This means problems might only be noticed after something goes wrong. Robots can do more tasks in bigger quantities with AI, usually through sensors connected to the internet. For instance, robots can pick orders in warehouses or care for crops in fields.

Bonus: Computer Vision

Computer vision is all about teaching computers to “see” and understand pictures and videos in a smarter way. Instead of just recognising them, it helps computers extract useful info from images and videos using sensors and smart tricks. 

This info can then be used to automate jobs or learn new things. It’s like giving computers eyes to understand the world and make choices based on what they see. 

For example, self-driving cars use computer vision to drive safely. It can even predict stuff by looking at data, like peeking through walls or around corners.

SAP AI – Where Can It Be Used?

Each year, more companies are recognising the advantages AI offers for their operations. Sectors like healthcare and banking, with large data sets, saw the potential of AI early on. Now, AI’s versatility makes it applicable across various business models. 

Here are a few examples of industries benefiting from AI.

1: AI in Banking

Banks and financial firms were among the first to embrace AI for tighter security, following their need for fast transactions and strict compliance. 

With AI bots, digital payment guides, and biometric fraud checks, they enhance efficiency, customer care, and cut down on risks and fraud.

2: AI in Healthcare

AI in healthcare is all about using massive sets of medical data to understand how different treatments affect patients. Hospitals are also using AI to improve staff and patient happiness and save money.

3: AI in Manufacturing

In an IoT network, devices and machines share data through a central system. 

AI steps in to not just handle this data, but also to anticipate both chances and hitches, and to streamline tasks and processes accordingly. 

In smart factories, this means customising 3D printing jobs as needed and managing virtual stocks efficiently.

4: AI in Retail

The pandemic shifted shopping habits, pushing more people towards online shopping. Retail has become fiercely competitive and dynamic. 

With various online interactions, tons of data is generated. Retailers are now embracing AI to decipher this data, aiming to understand customers better and fulfil their needs promptly.

What is SAP AI Core?

SAP AI Core is a service within the SAP Business Technology Platform that helps manage and operate your AI tools flexible and scalable. 

It integrates smoothly with SAP solutions, allowing you to implement various AI functions using popular open-source frameworks. 

With SAP AI Core, you can confidently make data-driven decisions tailored to your business needs. It efficiently handles large data volumes and offers scalable machine learning (ML) capabilities for automating customer feedback analysis and classification tasks. 

It also offers preconfigured SAP solutions, open-source machine learning tools compatibility, and seamless integration with other applications.


Businesses increasingly rely on AI to stay competitive in today’s rapid markets. SAP’s AI tools assist companies in automating tasks, improving processes, and increasing efficiency across various areas such as customer service, manufacturing, and finance. 

These solutions analyse real-time data, identify trends, predict issues, and improve product offerings.