Generative AI in the SAP ecosystem made simple

Introduction to Generative AI

We've been talking about AI(Artificial Intelligence) for years, but for the most part, we've known it as the technology that predicts. It could tell you if it was likely to rain tomorrow, help doctors catch illnesses early, or adjust a lesson plan so a student could learn better. Back then, AI's primary job was to predict by reading patterns in the present and imagine the most likely future. It is called Predictive AI. Recently, there has been significant change in the narrative. A new kind of AI called Generative AI has stepped into the spotlight, which doesn't just recognize patterns, but uses them to make something fresh, something that has never existed before.

What is Generative AI?

Generative AI creates content such as original text, pictures, music, videos, and other types of data by learning the foundational patterns and structures in the data it was trained on, using deep learning techniques and sizable datasets. After being trained, generative AI models can generate unique content in response to user input, which is frequently in the form of requests in natural language. The content can be so convincing, so uniquely expressive, that you might not know they didn't come from human hands or minds.

Foundational models for today’s Generative AI

Transformers

Prior to 2017, Natural language processing models, including Long Short-Term Memory networks and Recurrent Neural Networks had drawbacks. They were costly to train, processed text in a sequentially, and had a problem understanding long documents or multi-step reasoning. The Transformer model followed and changed everything. It made understanding modern languages possible.

How Transformer changed everything

• Attention Mechanism: The model can simultaneously "look" at every word in a sentence and assess how important each one is relative to others. For example, in the sentence:‍

"The drug had expired, so it got disposed of."

A transformer can directly link "it" to "drug", even if there are many other words in between.

• ‍Parallelization: By processing sequences in parallel, training and inference are now faster.‍
• Scalability: Transformers improve with size, opening the door for the enormous language models of today.

Common Transformer-based models

BERT model– This is suitable for understanding context and meaning.

GPT model– This is suitable for generating coherent, long-form text.

PaLM – It is Google's extensive language model for coding and reasoning

Transformers are the engine under the hood of almost every advanced AI model today, including LLMs like GPT, Claude, and Gemini.

Diffusion Models

Earlier image generation techniques often failed to adhere to the input prompt or produced blurred, unrealistic results. Businesses couldn't count on them to produce brand-quality work.

Two phases of Diffusion models:

1. Training phase:  During the training phase, a real image is used, and noise is added gradually over a number of steps until the image is just static.
2. Generation phase: During the generation phase, a prompt guides the process backwards from pure noise until a clear, coherent image appears. This allows the model to control fine details at every stage.

Business uses of Diffusion models

• Marketing creative scan generate product shots, lifestyle photos, or background variations without employing a photographer for every iteration.
• Spare-part diagram scan create neat, labeled technical drawings for inventory systems or service manuals.
• Design prototyping allows quick visualisation of ideas before investing in costly production.

Large Language Models (LLMs)

These are base models trained on vast volumes of datasets and are refined for the following purposes:

• Helpfulness (good adherence to instructions)
• Safety (avoidance of negative response)
• Specific skills (coding, reasoning, summarization)

Because they can read, write, and plan, they are referred to as general-purpose problem-solvers.

Scope of Generative AI

The scope of Generative AI is broad:‍

• Text: From travel itineraries to thought-provoking essays and personalized letters.
• ‍Images: Concept art, event posters, or realistic fashion designs.‍
• Audio & Video: Original background music, immersive audiobooks, or training videos made from scratch.‍
• Code: Building custom chatbots, web tools, or even entire platforms in minutes.‍
• Data: Generating realistic but fictional patient profiles for medical research, or creating test data for complex simulations.

Benefits of Generative AI

Generative AI is rapidly increasing creativity and productivity. For forward-thinking companies, it's less about whether they should care and more about how fast they can adapt.

Across industries, companies are seeing three big shifts:

1.Efficiency that gives time back to people

Think about the routine work, such as drafting meetings, summaries, preparing standard reports, and organizing information, that consumes a significant amount of time. Generative AI can do these in minutes, allowing teams to focus on strategy, problem-solving, and creativity.

For example, a legal department can instantly produce tailored contract drafts that follow the company's standard clauses and formatting, cutting what used to take days down to minutes.

2.Innovation at a scale humans alone can't match

Creativity often takes time. Generative AI compresses that timeline by producing dozens or even hundreds of variations in seconds.

That’s why a product development team can generate multiple prototype designs for a new gadget overnight, which engineers can refine and test the best design, bringing the product to market much faster.

3. A more personalized user experience

Rather than relying on one-size-fits-all solutions, Generative AI can shape interactions, content, and recommendations around each customer's unique situation.

For example, a travel company can auto-generate a custom itinerary for a family, factoring in their interests, budget, travel history, and even the local events during their trip.

Generative AI isn't replacing human intelligence, but only amplifying it. For enterprises, it's the difference between working harder and working smarter, between chasing innovation and leading it.

Generative AI use cases across industries

1. Healthcare: Doctors can use Generative AI to draft patient diagnosis notes by summarizing key findings from medical records in plain language.
2. Finance: Generative  AI can minimize the time required to prepare financial reports from days to minutes by combining contextual insights with raw data.
3. Entertainment:  AI can generate game environments, music, and video scripts.
4. Customer  service: Complex consumer inquiries are being handled by generative  chatbots, which provide human-like responses.
5. Manufacturing:  By producing optimized product blueprints, generative design tools lower the cost of materials.

Core Technologies Powering Generative AI

When you see generative AI create realistic images, human-like text, or even workflows that are ready for business, it seems magical. However, it's all math, algorithms, and intelligent engineering behind the scenes.

Let's examine how the five core technologies that enable it all work in enterprise settings like SAP.

Large Language Models (LLMs)

LLMs are huge AI models trained on massive datasets so they can understand and generate language similar to that of humans.

Examples of LLMs

GPT-4: It is very powerful and all-purpose.

• ‍LLaMA (Meta): This model is open-source and easy to adapt for research.‍
• Mistral – It is smaller and faster, but still powerful.‍
• Claude: Also known as Anthropic, this model focuses on alignment and safety.‍
• Domain-specific LLMs– This model is trained for a specific field. For example, a finance LLM trained on accounting rules.

LLMs require an architecture called RAG (Retrieval-Augmented Generation) to enhance their capabilities. It combines a generative model LLM with an information retrieval module.

The two actions RAG performs are:

Retrieve: It looks up relevant details in an outside knowledgebase.

Augment: The relevant details retrieved are provided to the LLM in the prompt so it can generate an informed response.

Multimodal AI

Human beings can perceive and communicate through multiple senses. Multimodal AI extends this capability to machines by narrowing the gap between artificial and human perception. Multimodal AI can process and generate various kinds of data with ease such as:

• Text-to-Image
• Text-to-Audio
• Text-to-Video
• Image-to-Text

Embeddings

AI uses embeddings to give numbers meaning. Words are mapped to high-dimensional vectors so that semantic similarity can be computed, rather than being stored as strings.

Importance of Embeddings

• It allows for semantic search, which finds content by meaning rather than precise keyword matches.
• In embedding space, sentences or words with similar meaning have vectors close together.

Prompt Engineering and Instruction Tuning

Generative AI systems are powerful, but the accuracy of their results depends on how you use them.

Prompt engineering involves the creation of inputs to direct the model toward the intended result.

Instruction tuning goes one step further by training models on specific instruction-response pairs so that, without complex prompts, they naturally follow human intent.

AI Orchestration Platforms and AI Agents

Despite the strength of LLMs and multimodal models, most real-world applications require orchestration. It is the process of coordinating various AI tools and capabilities to accomplish a task.

Orchestration transforms generative AI from being the passive assistant into an active collaborator which is capable of solving complex, multi-step problems. Examples of AI orchestration platforms are LlamaIndex, LangChain, and Microsoft Semantic Kernel. They enable developers

• Chain several AI calls together
• Integrate with third-party APIs and databases
• Make workflows that are reusable.
• Manage memory and the context of the conversation.

AI agents take this further by acting independently to achieve a goal. An AI agent is capable of:

• Planning tasks
• Selecting the appropriate tools or APIs.
• Executing steps in sequential order
• Learning from feedback

Architectural Considerations for Generative AI in Enterprise

Generative AI has so much benefit to offer. As a result, companies everywhere are assessing how to integrate it into their architectures. But, implementing AI on a large scale calls for careful architectural decisions around infrastructure, integration, security and compliance.

There are four major architectural factors companies consider when implementing generative AI solutions. They are:

1. On-Premise AI vs. AI in Cloud Architectures

One of the strategic decisions businesses make is about where to run generative AI workloads. They choose between on-premises AI and AI in Cloud architecture

2. API-based AI Consumption Models

Many organizations are using AI consumption models based on APIs. In this model, providers make generative AI capabilities available via secure endpoints.

Advantages

• ‍Faster integration: Companies can integrate AI into existing applications with little interruption by using APIs.‍
• Advanced model access: Users have access to the most recent, regularly updated models.
• Cost-effectiveness: Companies don’t have to invest in infrastructure, they only pay for usage.

Disadvantages

• ‍API Provider lock-in: A heavy reliance of a company on a single API provider creates a strategic risk, making it difficult and costly to switch providers later.‍
• Limited customization: Domain-specific fine-tuning may not be fully supported.‍
• Risks of data exposure: During API calls, a company’s private data may cross organizational boundaries.

3. Hybrid AI Techniques (Combination of RPA, predictive models, and generative AI)

Although generative AI has much to offer, you shouldn't use it alone often. A hybrid AI approach that integrates generative models with pre-existing automation and predictive systems is often needed for real-world business use cases:

Below are three possible hybrid AI techniques:

• Generative AI & Robotic Process Automation (RPA): Generative AI is capable of interpreting unstructured data such as documents, and emails. All structured actions are assigned to to RPA bots for execution in HR systems or ERP.
• Generative AI & Predictive Models: Predictive AI models are helpful at predicting outcomes such as demand for a product. But, generative AI improves decision-making through the generation of reports, simulations, etc.‍
• Generative AI as an Orchestrator
  Generative AI goes beyond simple task assistance. It can serve as a central intelligence layer coordinating AI systems, APIs, and workflows so business processes run more efficiently and consistently

4. Security, Privacy, and Compliance Considerations

Because generative AI systems often manage vast volumes of sensitive data, security, data privacy, and compliance are non-negotiable

AI and security:

• There is a need to encrypt data both in transit and at rest to restrict unauthorized access.
• Implementing role-based access controls (RBAC) so users have access to only what is assigned to them. This is capable of limiting model and data usage.
• Employing AI-specific monitoring to identify adversarial attacks, model poisoning, or prompt injection.

Ai and data privacy:

• Before sending personal information to AI models, make sure you anonymize or mask it.
• Follow the guidelines for data minimization. Ensure you send only what is required.
• Put in place "right to be forgotten" procedures for training or fine-tuning data.

AI and compliance:

• Industry-specific regulations such as GDPR and PCI DSS must be taken into consideration.
• In AI-driven workflows, audit trails and logging are critical and prove compliance.
• Emerging AI governance standards will require transparency and risk classification.

What is Generative AI in SAP?

Generative AI is quickly becoming a fundamental force driving the way businesses operate and innovate. In the SAP ecosystem, Generative AI extends the functionality of core systems such as SAP S/4HANA, SAP BTP, and SAP Integration Suite, making it possible to automate repetitive tasks, generate intelligent insights, and create personalized user experiences.

Use cases of Generative AI in SAP Ecosystem

Generative AI can be deeply integrated into core SAP ecosystem processes to improve the intelligence and usability of ERP systems.

A few touchpoints are as follows:

1. SAP S/4HANA Automated Financial Statements: Finance team can use AI to generate narrative  commentary and variance analysis straight from real-time SAP data, removing the need for manual compilation of quarterly statements.
2. Drafting Procurement Contracts: Generative AI could generate draft supplier contracts in SAP Ariba using already established terms, risk profiles, and  clauses.
3. Documentation for Maintenance: By analyzing past service records, generative AI in SAP Asset Management could     generate precise maintenance work instructions.
4. Customers Engagement: AI-generated, customized email responses for customer tickets could be sent to service agents in SAP Service Cloud, accelerating resolution times while increasing customer satisfaction.

The secret is to combine the creative powers of Generative AI with SAP's structured enterprise data, which transforms transactional user interactions into conversational ones and transforms static reports into actionable, human-readable insights.

Generative AI integration points with SAP

1. SAP S/4HANA

S/4HANA, the digital core of SAP drives key business processes. It comes in two editions

Public Cloud: A cloud ERP solution designed to help companies standardize and automate their core business processes using industry best practices

Private Cloud: A cloud ERP solution that an organization can tailor to their unique business needs.

Both editions easily integrate AI into their operations.

2. SAP Business Technology Platform (BTP)

Think of SAP BTP as the AI innovation hub. SAP BTP offers a unified platform for data, integration, analytics, automation, and AI services needed to build innovative business applications. Complex AI models could be reliably integrated at scale by SAP developers and partners by using tools like SAP AI Core and AI Foundation.

3. Generative AI across SAP modules

One interesting aspect of generative AI is the ability to work across different business functions:

• Finance: AI explains complex journal entries, crafts detailed narrative reports, or detects anomalies.
• Supply   Chain:  AI can improve demand forecasting, draft responses to supply disruptions, and produce personalized vendor communications templates.
• HR: HR teams can use AI to improve the employee experience through AI-driven job descriptions and personalized learning journeys.
• Sales  & CRM: Sales teams get AI assistance in creating tailored proposals, nurturing  leads intelligently, and summarizing customer conversations across different channels.

SAP BTP Generative AI

1. SAP AI Core & AI Foundation

SAP AI core is a capability in SAP BTP which provides the runtime environment to train, deploy, and manage AI models. AI Foundation provides integration layers and pre-built services.

2. External AI Integration

Businesses can integrate BTP with external LLM APIs like OpenAI to:

• Generate insights from conversation directly in SAP Fiori apps.
• Draft contracts, purchase orders, or HR letters.

3. Developer empowerment

Using SAP’s Cloud Application Programming (CAP) Model, SAP developers can extend SAP solutions with generative AI in a modular way.

4.  Event-driven AI with Kyma

SAP developers can run containerized workloads. When you combine Kyma with event-driven architectures:

• Supply chain events can trigger AI-driven mitigation strategies.
• HR onboarding events can trigger AI-driven customized employee communication.

How Generative AI impacts SAP integration & automation?

1.  AI-Driven Integration Flows in SAP Integration Suite

SAP Integration Suite connects SAP and non-SAP systems. With AI, natural language descriptions are used to generate and auto-suggest integration flows.

For example,

"Create an SAP Cloud Integration flow that receives an HTTP POST request with customer data in JSON format and posts it to the S/4HANA OData service/API_BUSINESS_PARTNER to create a new Business Partner. Add logging and error handling."

STEPS:

1. Choosing Integration flow in integration suite

2. Choosing the option, Generate Integration with assistance from AI

        3. Describing Integration Scenario, then clicking the Send button

          4.  Following the next steps will generate a ready-to-use iFlow.

2. Generating integration mappings with LLMs

Traditionally, creating mappings between systems (XML, JSON, IDoc, etc.) is often a challenging process. Generative AI can:

•  Propose field mappings automatically.
• Generate Groovy scripts for transformations.
• Validate mappings through analysis of past patterns.

3. Automating API documentation and testing

SAP developers often spend several hours documenting APIs and writing test cases. With generative AI:

• Integration flows can be used to auto-generate API specifications.
• Unit tests can be suggested, reducing time-to-market.

For example, an AI assistant generates OData service documentation in minutes.

4. Intelligent bots with SAP Build Process Automation + Generative AI

SAP Build Process Automation enables low-code workflows and bots. When generative AI is integrated, bots can create HR reminders, procurement follow-ups, and customer emails, helping employees work faster and smarter.

Conclusion

Today, businesses' approaches to automation, innovation and interaction with data are being transformed by generative AI. In the SAP ecosystem, Generative AI extends the functionality of core systems like SAP S/4HANA, SAP BTP, and SAP Integration Suite making it possible to automate repetitive tasks, generate intelligent insights, and create personalized user experiences.

Ultimately, generative AI in SAP is helping businesses move from routine execution to strategic innovation and setting them up for success in the era of intelligent enterprise.

FAQs

What is a large language model?

Answer: Large language models (LLMs) are huge AI models trained on massive datasets so they can understand and generate human-like language. Some examples are GPT-4, and Meta.

How does Generative AI apply to the SAP ecosystem?

Answer: The applications of Generative AI in SAP ecosystem are many.

It speeds up decision-making across ERP systems, they include:

• Automating procedures,
• Delivery of business insights,
• Generating reports,, and
• Helping users with natural language queries.

Can I bring my own LLM to the SAP ecosystem?

‍Answer: Yes. SAP BTPAI Core and AI Launchpad allow you to deploy and integrate your own models into SAP workflows.

What is prompt engineering?

Answer: Prompt engineering involves the creation of inputs to direct the model toward the intended result.

‍

Related Article

How to develop a full-stack application in SAP Build Code using SAP Joule and Generative AI?