Understanding AI Terminology

The term "artificial intelligence," or "AI," refers to systems that can simulate human abilities, such as understanding language, recognizing images, or solving problems.

You’ve probably used AI today without realizing it. Some examples could be represented as the following:

• An e-commerce website chatbot answering your question
• Unlocking your mobile device with your face
• A digital assistant recommending your next meeting

Data is the foundation of AI—but not all data is created equal. Structured data is clean and organized. As an example, you can think of rows and columns, like a spreadsheet.

Examples:

• Sales Orders
• Customer IDs
• Inventory Levels

Structured data is what SAP solutions, like SAP S/4HANA, are built to manage, such as clean, consistent data that can feed AI models for better business decisions.

Machine learning is a term that represents how computers learn from data without being explicitly guided. Instead of writing rules like "if this, then that," machine learning models learn from examples.

In SAP applications, machine learning:

• Classifies support tickets by urgency or topic
• Predicts late deliveries in the supply chain
• Detects suspicious transactions in financial data

As an example, you can consider machine learning as the engine behind many of SAP’s intelligent features. It learns, adapts, and improves with use.

Deep learning takes machine learning to a higher level by using neural networks and algorithms, which are modeled loosely after how the human brain processes information.

Deep learning is especially good at handling unstructured data, such as the following:

• Images (scanning and sorting documents)
• Voice (understanding spoken commands)
• Text (analyzing employee feedback)

As a business example, SAP uses deep learning to analyze customer sentiment from survey responses or power chatbots that understand natural language.

Generative AI is the most exciting and creative stage in AI’s evolution. It’s not just about analyzing data anymore. AI can now generate new content from reports and emails to business insights.

Gen AI is successful for the following reasons:

• Built and Trained by Large Language Models (LLMs): These models are trained on vast amounts of text and can generate human-like responses, summaries, and suggestions.

• Creates instead of just classifying: Gen AI can write, summarize, translate, and help users communicate more effectively.

• Joule: the SAP Business AI copilot
  • Joule is SAP’s generative AI assistant, embedded directly into tools like SAP S/4HANA and SAP SuccessFactors.
  • Ask Joule questions like "What’s our sales trend this quarter?" and receive clear, helpful answers.
  • Joule can draft emails, summarize reports, and suggest next steps inside workflows.

AI Agents are autonomous programs designed to perform specific tasks by perceiving environments, data, and intent. In a business context, AI Agents can coordinate across systems, learn from feedback, and adapt actions to support complex workflows.

SAP is developing generative AI Agents that work behind the scenes to handle tasks like:

• Resolving invoice disputes
• Automating procurement workflows
• Managing supply chain issues

SAP’s generative AI is grounded in trusted data by using secure, enterprise-grade models and real-time business data so that you and your customers can trust the results.

• AI is the big idea—the dream of creating machines that can think, learn, and help us solve real-world problems.
• Machine learning, deep learning, and generative AI are the building blocks that make that dream a reality—each one adding more intelligence, adaptability, and creativity.
• Structured data is your launchpad. It’s what fuels these systems, turning raw information into smart, data-driven action.
• The layers work like a ladder:
  • Generative AI builds on Deep Learning
  • Deep Learning builds on Machine Learning, and it all lives under the umbrella of AI.