SAP Community - Artificial Intelligence

Has Joule cross business-role data bleeding been addressed, and how?

2025-03-28T11:12:49.046000+01:00

With the case of Joule being embedded throughout the core of SAP S/4HANA Cloud Public ecosystem, it's natural to assume that Joule has access to all the data in the underlying datastores - which makes sense. However, as with any LLM there substantial considerations regarding bleeding data, either directly or as derivatives. I'm not worried about out-of-organisation security issues, more so cross-organisation security complications.

I have been looking around for articles or blog posts regarding Joule's grasp on this / measures in place to prevent bleeding, but I haven't found any.

In the most simple case, imagine two business roles;

- Junior Procurement/Sourcing

- Senior Procurement/Sourcing

The only difference is the junior does not have access to supplier contact information to protect business IP. Given both roles have access to Joule, I would expect outputs of Joule to be governed by similarly to defined access rights in the individual Business Role Access Categories.

In this case, access rights to specific fields are relatively simple, and could essentially be dealt with in the same way as LLM's deal with copyright material / LORA's etc. However, this becomes increasingly complex, and is by no means trivial, as the amount of business roles, data access, data and output types increase.

So:

1. How - technically - does Joule enforce the same minimal-access principles as there exists around RBAC?

2. Does there exist generalized analytics/monitoring around Joule interactions to identify and act upon data-bleeding instances?

3. Does Joule always work across tenant-data, or is "Joule" an individual agent per. user/union of unique business role combinations scoped to only have access to the data a business role(s) has access to, and is it actively designed to prevent access to more data in the context window?

Unless there exists a formal article on the technical implementation of this, which we can refer to with regards to data sensitivity, it must be assumed that data bleeding is at best possible and at worst not seriously considered in Joule.

Exploring SAP Joule Integration Options with SAP Datasphere

2025-04-14T15:07:03.690000+02:00

Hello SAP Community,

I'm exploring the possibility of integrating SAP Joule with SAP Datasphere — either via direct service availability or custom architecture.

Specifically, I’m interested in understanding:

Is SAP Joule currently accessible as a standalone service on SAP BTP or only embedded within specific SAP applications?
Are there any supported or recommended approaches for using Joule’s capabilities in conjunction with SAP Datasphere?
Does the roadmap include broader availability of Joule APIs or SDKs that developers could leverage for custom integrations?

I’d appreciate any guidance, documentation links, or experience others have had exploring this kind of setup.

Thanks in advance!

Joule SAP Datasphere

Questions about SAP Joule

2025-04-15T07:07:51.942000+02:00

Hello SAP Community,

We are planning to enable SAP Joule. But before we proceed, we wanted to understand the capabilities of Joule with respect to SAP Sales Cloud Version 2. I had a few questions regarding it, please read through them and provide your insights.

I understand that SAP Joule currently has 3 Interaction Patterns: Transactional, Navigational and Informational. But I would like to know more about the following:

1. What are the Business Objects supported in Joule with respect to SAP Sales Cloud Version 2?
2. What are the use-cases that are supported in Joule with respect to SAP Sales Cloud Version 2?
3. What are the current limitations of Joule?
4. Is there a way to customize navigation capabilities of Joule from SAP Sales Cloud Version 2 to SAP ECC?

If there are any online sources that I could reference for the questions above, please provide those to me as well.

Thanks in advance,
Karthik C M.

AI Agent

2025-04-29T08:42:17.144000+02:00

Dear SAP Community,

I would like to inquire regarding the availability and usage of AI Agents within SAP environments.

Are AI Agents available for both SAP Public Cloud and SAP Private Cloud editions?
Could you kindly provide guidance on the prerequisites, activation process, and how to utilize AI Agents in both cloud models?

I appreciate your support and look forward to your insights.

Best Regards,

Darma

SAP Business AI - Where to start?

2025-05-05T20:35:02.409000+02:00

SAP Discovery Center: Your Gateway to AI-Powered Innovation

In today's rapidly evolving digital landscape, organizations need a centralized platform to discover, evaluate, and implement innovative technologies. The SAP Discovery Center serves this purpose, functioning as a comprehensive gateway to SAP's vast ecosystem of solutions, with a particular emphasis on artificial intelligence capabilities.

What is SAP Discovery Center?

SAP Discovery Center is a dynamic online platform that serves as your primary resource for exploring and implementing SAP Business Technology Platform (BTP) services and AI features. It provides structured guidance for creating tailored business solutions that address specific organizational challenges.

As stated on the platform itself, SAP Discovery Center positions itself as "Your Gateway to Innovation," helping users to "Discover, evaluate, and adopt SAP Business Technology Platform services and AI features to create tailored business solutions."

Key Components of SAP Discovery Center

The Discovery Center is organized around three core pillars:

1. Discover

Browse comprehensive catalogs of:

2. Evaluate

Identify and assess the most suitable solutions with tools like:

Service Estimator
AI Estimator
Price and Value Estimates

3. Adopt

Implement solutions with:

Quick Account Setup
Proof of Concept Creation
Expert Collaboration
Initial Setup Guidance
Guided Project Board

The AI Feature Estimator

One of the most valuable tools within the Discovery Center is the AI Feature Estimator. This tool helps organizations:

Generate estimates for AI features
Modify estimates as requirements evolve
Share estimates with stakeholders
Understand the potential business impact of AI implementation

Related Resources

SAP Discovery Center connects users to a broader ecosystem of resources:

SAP Help Portal
SAP BTP Guidance Framework
Business Content
Customer References
Tutorials
Communities
Road Map Explorer
Partner Applications

Why SAP Discovery Center Matters for AI Implementation

For organizations interested in implementing AI within their SAP landscape, the Discovery Center provides several unique advantages:

Centralized Information: A single source of truth for all available AI features across the SAP portfolio
Guided Implementation: Step-by-step missions for specific AI use cases
Estimation Tools: Resources to calculate potential costs and value
Real-world Examples: Customer stories demonstrating successful implementations
Expert Support: Access to topic experts and community resources

By leveraging the SAP Discovery Center, organizations can accelerate their AI adoption journey, reduce implementation risks, and maximize the business value of their investments in SAP's AI capabilities.

The Importance of Artificial Intelligence Training for SAP Solutions

Artificial Intelligence (AI) is transforming how companies operate, enabling greater efficiency, process automation, and more accurate predictive analytics. Within the SAP ecosystem, AI plays a fundamental role in optimizing business processes, from financial management to customer experience.

Building AI expertise within the SAP context is not just a competitive advantage but a necessity for professionals who wish to drive innovation and extract maximum value from technological solutions.

Whether through predictive modeling, robotic process automation (RPA), or virtual assistants, AI in the SAP universe is opening new business possibilities. Therefore, investing in continuous learning and exploring specific training can be decisive for standing out in the market.

Exploring the SAP Discovery Center: Strategic Positioning in Artificial Intelligence

The SAP Discovery Center is an essential resource for any professional seeking to understand and implement Artificial Intelligence solutions in the SAP ecosystem. It offers a comprehensive view of use cases, integrations, and best practices, helping companies accelerate their digital journey with AI.

Through the Discovery Center, it's possible to access reference projects, explore services, and gain valuable insights on how to leverage AI capabilities within SAP solutions. This way, professionals and organizations can make more informed decisions and improve their innovation strategy.

List of Artificial Intelligence Training for SAP

Here is an organized list of essential training programs on Artificial Intelligence in SAP:

These training programs are fundamental for any professional who wants to master Artificial Intelligence in the SAP ecosystem and enhance their business solutions.

Send your doubts!

Detecting Handwritten vs. Digital Docs in SAP CAP + DOX

2025-05-09T08:46:00.939000+02:00

I'm working on a solution using SAP CAP (Cloud Application Programming) with Node.js, integrated with SAP Document Information Extraction (DOX). This project sends documents via API to the DOX service to extract relevant information from files such as invoices and receipts. My requirement here is to check whether the document is handwritten or computer-generated. This distinction could significantly enhance data accuracy and processing logic. If anyone has insights on integrating handwriting detection with SAP DOX or using pre-processing techniques for document classification, I’d love to connect and learn more.

SAP BDC Demo Environment

2025-05-21T10:07:19.202000+02:00

Hello All,

Since the BDC trial version is now available, I’d like to create a demo using it. Could you please guide me on how to enable or build the content? Also, is it possible to showcase a demo using standard content, considering that the source systems are not properly maintained? I’m looking for any way to effectively pitch it to clients. I’m exploring options to work exclusively within BDC AI/ML part. The practice source systems are not properly maintained, making it impractical to establish connections with them. However, intelligent applications or insight apps require both source and target systems to be connected in order to enable the content. Are there any materials or documents available that can guide the implementation of Databricks AI/ML on raw data within an SAP BDC environment?

Need information on different SAP AI modules/Resources

2025-07-09T15:44:37.948000+02:00

I am preparing for the SAP AI certification and went through all the relevant course material. I still need clarification on some concepts. I have put my understanding of different SAP AI resources with an example. Could someone clarify if my understanding is correct ?

SAP AI Module Primary Role What It Does Examples

SAP AI Core	Manages end-to-end lifecycle of any ML model	- Train, deploy, monitor containerized models- Define YAML pipelines, executables, secrets	🔹 Train an MRP delay prediction model using TensorFlow🔹 Monitor PO exception forecasting
SAP Generative AI Hub	Connects to external LLMs via an abstraction layer	- Prompt execution- Orchestration- Safety filters- Document grounding	🔹 Ask GPT-4 to summarize blocked invoices in S/4HANA🔹 Use Claude to explain a supply issue
SAP AI Launchpad	Central UI for managing all AI assets (Core + GenAI)	- Deployment dashboard- Manage pipelines, prompts, templates, secrets	🔹 See status of ML model runs🔹 Manage GenAI Hub prompts and results in one place
SAP AI Foundation	Infrastructure layer that powers Core & GenAI	- Resource orchestration (e.g., CPU, GPU)- Security, observability, scaling	🔹 Supports storage access to datasets in Azure blob🔹 Enables auth and runtime allocation
SAP AI Business Services	Pre-built APIs for common AI tasks	- Pre-trained services like: ▪ Document Information Extraction ▪ Service Ticket Intelligence	🔹 Auto-extract invoice fields from PDF🔹 Categorize helpdesk ticket based on past text data
SAP HANA Vector Engine	Embedding & vector search for RAG or semantic search	- Stores embeddings- Enables Retrieval-Augmented Generation (RAG) pipelines	🔹 Query internal policies grounded to document embeddings🔹 Match customer queries to SOPs
SAP BTP SDK for Generative AI (Python)	SDK to call GenAI Hub/Orchestration APIs from code	- Define LLM, prompt, and deployment in Python- Access orchestration APIs	🔹 Call GPT from a script to validate MRP output🔹 Automate prompt evaluation over vendor data

Can Joule AI Make SAP Readiness Check & Process Insights Smarter?

2025-08-12T18:20:14.608000+02:00

Hi everyone,

I’m Tommy, an intern at SAP. As part of my internship, I’ve been given the research topic:

"How to utilise Joule to interpret SAP tools such as Readiness Check and Signavio Process Insights"

After discussions with the original topic proposer, I’ve refined this to:

"Leveraging Joule AI for immediate, context-aware interpretation of SAP tools such as Readiness Check and Process Insights outputs through integrated or standalone models."

Current ideas under exploration:

Potential role of AI (Joule):

Integrated approach – Joule embedded directly in tools for live Q&A and guidance.

Standalone approach – Central Joule portal where exported tool data is uploaded for AI-generated summaries.

Industry & process-specific insights:

Could Joule be trained using Signavio Process Explorer as a knowledge source?

Customer-specific process learning:

Could Joule learn a customer’s business processes over time to adjust tool benchmark thresholds?

For example, if a process is flagged as problematic by a benchmark but is actually fine for a specific customer, could Joule adapt to avoid false alarms?

I’m not looking to start a conversation and see if this direction aligns with others’ perspectives and see if anyone has experience in the topic.

Would love to hear your thoughts!

SAP Business AI – Driving the Next Wave of Enterprise Innovation

2025-08-17T04:48:19.355000+02:00

Why It Matters

AI is no longer a distant concept—it’s transforming how businesses operate today. Generative AI alone is projected to add up to $4.4 trillion in global productivity within the next five years. For SAP customers, who generate 87% of the world’s commerce, the impact of embedding AI directly into core business processes is both immediate and profound.

Key Highlights:

Joule – The AI Copilot

Acts as SAP’s unified front end, enabling users to work in natural language.
Delivers context-aware insights, transaction execution, and navigation across SAP apps.
Boosts efficiency by up to 80% for employees.
Coordinates specialized AI agents for complex tasks (e.g., dispute resolution, sustainability planning).
Integrates with Microsoft Copilot for Office 365 for seamless cross-platform experiences.

Embedded AI – Innovation at Scale

AI features are built directly into SAP’s cloud solutions (ERP, Supply Chain, HCM, CRM, Procurement, etc.).
Use cases already improving business outcomes:
- Henkel → Faster, data-driven decisions with AI in SAP Analytics Cloud.
- FC Bayern Munich → Accelerated recruiting with generative AI in SAP SuccessFactors.
- Gibson → Boosted email revenue by 50% through AI-powered customer engagement.
Over 100+ new AI features released in 2024, with continuous quarterly rollouts.

AI Foundation – Building Custom Solutions

Part of SAP Business Technology Platform (BTP).
Provides developers with reusable AI services, lifecycle management, and access to leading LLMs (OpenAI, Google, AWS, Meta, etc.).
Offers deep integration with SAP HANA Cloud Vector Engine and SAP Knowledge Graph for contextualized business data.
Enables low-data AI innovation, such as invoice due date predictions or sales order auto-filling .

Responsible AI at SAP

Commitment to ethics, privacy, and security:
- No partner or third-party models are trained on customer data.
- AI ethics embedded in governance via SAP’s AI Ethics Steering Committee.
- Guardrails against misuse, bias, and security risks.
Alignment with UNESCO AI ethics standards and EU AI Act compliance .

SAP’s vision is AI-first and suite-first:

AI copilots like Joule will become hyper-personalized.
More AI-driven automation across finance, HR, supply chain, and sustainability.

The future is already here. With SAP Business AI, organizations can run better, innovate faster, and empower people to focus on what truly matters.

Explore more:

Error

2025-09-06T09:38:22.178000+02:00

error

Finding SAP Note using AI

2025-09-06T09:48:14.448000+02:00

The Challenge Every Basis Administrator Knows

If you’ve worked in SAP Basis, you know the scenario well:
A transport fails, a short dump appears in ST22, or a background job terminates.

The next step? Hunting through SAP Notes in the Launchpad.

This process is often:

Time-consuming, with hundreds of possible Notes.
Keyword-driven, meaning you need to guess the right search terms.
Trial-and-error, as many Notes don’t exactly match the error in your system.

The result is hours of lost productivity, delayed fixes, and frustrated teams.

Today’s SAP Notes search is powerful, but it depends heavily on manual filtering. Notes can be technical, fragmented, or reference outdated versions of components. Even experienced Basis administrators often spend too much time narrowing down the “right” Note.

At the same time, SAP landscapes are becoming more complex, spanning S/4HANA, cloud integrations, DB migrations, and hybrid infrastructures. This complexity only increases the challenge of quickly mapping system errors to the correct Note.

Where AI Can Make a Difference

This is where Artificial Intelligence (AI) and Natural Language Processing (NLP) can transform the way we work with SAP Notes.

Imagine a system that could:

Read your error log automatically (from ST22, SM21, STMS, or job logs).
Understand the context of the error beyond keywords (e.g., system version, module, DB type).
Recommend the top 3 SAP Notes most relevant to fixing that exact error.
Learn from history. If a Note solved the issue for your team in the past, it would prioritize that next time.

This would save Basis teams valuable hours, reduce downtime, and make troubleshooting far more efficient.

How It Could Work in Practice

Log Capture: System errors, dumps, or transport logs are collected (e.g., via Solution Manager or Focused Run).
AI/NLP Engine: An AI model trained on historical SAP error messages and Notes embeddings interprets the log text.
Notes Recommendation: The system suggests a short, ranked list of relevant Notes.
Feedback Loop: Administrators confirm whether the suggested Note fixed the issue, improving recommendations over time.

This approach transforms troubleshooting from manual search to AI-powered recommendation.

AI-powered Note recommendation is just the beginning. Over time, such a system could evolve into an intelligent assistant that:

Correlates SAP + OS + DB logs for cross-layer analysis.
Validates system refreshes and upgrades by comparing logs before and after.
Predicts risks in transports or background jobs before they fail.

This vision aligns with SAP’s own push toward intelligent operations and embedded AI, but with a strong focus on the Basis layer, where today much of the work is still manual.

Final Thoughts

By applying AI and NLP to SAP Notes, Basis teams can move from reactive troubleshooting to proactive, intelligent support.

This is an area where I believe there is huge potential for innovation, research, and where every Basis professional can benefit.

I’m exploring this further and would love to connect with others interested in AI + Basis.

SAP Ecosystem - AI Rank Tracking for Suppliers

2025-10-23T18:37:52.281000+02:00

In 2025, enterprise decision-making is shifting from traditional search engines to AI-driven research.
Executives, procurement teams, and consultants are increasingly asking AI engines like ChatGPT, Gemini, Claude, and Perplexity questions such as:

“Best ERP platforms for manufacturing”
“Top SAP integration partners in Europe”
“Best solutions for supply chain automation”

The brands and solutions that appear in those AI-generated answers are the ones shaping real buying decisions. If your product or service isn’t showing up there, you’re invisible at the moment that matters most.

The best tool to track and optimize this new kind of visibility is www.airankchecker.net.

Tracks how often your company or SAP-related solution appears in AI answers
Monitors relevant keywords and queries across multiple AI engines
Gives actionable data to improve positioning in enterprise AI search
Ideal for SAP partners, integrators, and solution providers

This is not a marketing add-on. AI search is rapidly becoming a core channel in enterprise technology purchasing. The companies that optimize early will lead.

Has anyone here already started tracking their brand presence inside AI-generated answers?

Unable to Complete Practical Test- Applying the Value Story of SAP Business AI

2025-11-12T12:30:19.609000+01:00

Hello All,

I am planning to give the SAP certification exam

SAP Certified Associate- Positioning SAP Business AI Solutions as part of SAP Business Suite, however, I am unable to complete the practical test Applying the Value Story of SAP Business AI in the learning journey.

I have followed the steps as given in the video in the learning journey (Customer Return Management with SAP Business AI in SAP S/4 Hana Cloud) however still not able to complete the practical test as a result of which I am not able to give the final exam.

I have attached the screenshots for your reference, request your help for the same.

Kind Regards

Atul

How to properly configure SAP CAP MCP Server so Codex can detect it?

2025-12-05T15:55:31.483000+01:00

I'm trying to configure Codex to detect and use the SAP CAP MCP Server, but Codex keeps saying that no MCP server is running, even though I can start it manually and see it listening on a port.

Here is what I tried so far:

mcp.json

{
"servers": {
"cds-mcp": {
"command": "npx",
"args": ["-y", "@cap-js/mcp-server"],
"env": {},
"type": "stdio"
}
},
"inputs": []
}

The SAP CAP MCP server appears to run correctly (screenshot attached), but Codex still reports:

config.toml (Codex)

[tools.mcp-server-cap]
type = "mcp"
command = "cds-mcp"
args = ["--port", "4005"]

Question

Has anyone managed to configure Codex with the CAP MCP server?
Is there something obvious I'm missing. Perhaps related to TCP vs STDIO mode or how Codex expects MCP servers to be declared?

Any help or working example would be greatly appreciated.

How can GenAI be effectively used in SAP S/4HANA implementation and support projects?

2026-01-05T06:42:26.965000+01:00

Hello SAP Community,

I would like to understand how Generative AI (GenAI) can be practically applied within the SAP ecosystem, especially in S/4HANA implementation, migration, and support projects.

Specifically, I’m interested in insights on:

Using GenAI for requirements analysis and documentation
Automating test case creation and test data preparation
Supporting ABAP development, code review, and optimization
Enhancing functional consulting (FI, MM, SD, etc.) through AI-driven analysis
Improving incident management and ticket resolution in AMS/support scenarios
Any SAP-native tools or integrations (e.g., SAP Joule, SAP BTP + AI services)

I’d appreciate real-world examples, best practices, limitations to be aware of, and recommendations on where GenAI adds the most value in SAP projects.

Thank you in advance for sharing your experiences and suggestions.

Best regards,
Juveria

AI powered products - Alignment with SAP

2026-01-28T17:56:29.977000+01:00

Hi, I am designing and developing AI powered products specifically for SAP. I want to align with SAP to make that these products don´t already exist, and that they can eventually be integrated into SAP. The goal is to add value to SAP´s existing product portfolio, leveraging on Artificial Intelligence. Who can I contact in SAP?

Technical RFC: Building an "Auditable Autopilot" for Material Shortages – Where is the real Excel He

2026-01-29T15:37:07.353000+01:00

Hi everyone,

I’m an AI developer in Germany architecting a "Human-in-the-loop" agent to automate exception handling for discrete manufacturers (Mittelstand).

The goal is to reduce the 4+ hours of manual coordination that happens when a supplier is late or a carrier delay occurs, leading to potential line stops. We are focusing on a safe, auditable "exception autopilot" model rather than a generic "digital brain."

I have a few "reality check" questions for the consultants and experts here. I need brutal honesty so I don't build a product that fails in a real German factory environment:

The 'Shadow' System: When a delay hits, does the rescheduling happen in SAP (e.g., MD04/CO06) immediately, or do planners usually run a 'Shadow Excel' for 1-2 days first to figure out the impact?
The Data Gap: What’s the one piece of information that is never in SAP (e.g., real-time carrier GPS, unstructured email updates) that forces a human to manually 'bridge' the gap?
The 'Green Button' Barrier: If an AI drafted the SAP update and the supplier email, what is the #1 reason a German Plant Manager would not press 'Approve'? (e.g., legal liability, lack of audit trail, trust issues).
On-Premise vs. Cloud: For a mid-sized manufacturer, would the IT department demand this system runs 100% on-premise for data sovereignty, or is a highly controlled cloud connection (like SAP BTP) acceptable now in 2026?

Looking forward to the insights from the community so we can build something that actually solves the real pain points.

Thanks in advance for your expertise!

Question: Role-based access control in a chatbot using SAP Generative AI Hub

2026-02-02T21:35:03.841000+01:00

Hello SAP Community,

I’m preparing a pre-sales presentation for a real use case with SAP Generative AI Hub, and I want to address a security topic that customers typically ask about.

I have been searching for quite a long time for official documentation, concrete examples, or direct answers on this, but I have not been able to find a single clear reference that explains it conclusively.

My question is conceptual: when a chatbot consumes a model via Generative AI Hub and needs to retrieve business information (for example from SAP S/4HANA), how can we ensure that the responses respect the end user’s roles and authorization levels, and that no information is exposed beyond what the user is allowed to access?

I would especially appreciate:

Official SAP references (documentation, guides, or notes) that describe the recommended approach for access control and governance in these scenarios.
Expert recommendations on how to explain, at a high level, that end-to-end user authorizations are preserved (for example concepts such as identity/principal propagation), without going into configuration details.

Thank you for any guidance or references you can share!

PD:
I've done https://learning.sap.com/learning-journeys/solving-business-problems-using-sap-s-generative-ai-hub
And still, i'm not 100% sure on how roles,governance and security works.

How AI Is Improving Clinical Communication and Compliance in Life Sciences

2026-02-09T17:58:23.128000+01:00

Clinical communication across life sciences has become increasingly complex—spanning clinical trials, pharmacovigilance, regulatory submissions, and post-marketing safety. Teams are expected to process growing volumes of data while maintaining accuracy, traceability, and regulatory compliance.

Artificial Intelligence is helping address these challenges by improving how clinical and safety information is captured, structured, and shared. AI-driven automation can streamline adverse event intake, normalize data from multiple sources, support compliant documentation, and improve visibility across clinical and safety workflows.

In regulated environments, AI works best when combined with strong domain expertise and governance. Rather than replacing human judgment, it enhances consistency, reduces manual effort, and supports inspection readiness throughout the drug lifecycle.

I’d love to hear from the community:

Where have you seen AI deliver the most value in clinical or safety communication?

What barriers still exist when adopting AI in regulated life sciences settings?

Looking forward to learning from your experiences.

SAP Community - Artificial Intelligence

Has Joule cross business-role data bleeding been addressed, and how?

Exploring SAP Joule Integration Options with SAP Datasphere

Questions about SAP Joule

AI Agent

SAP Business AI - Where to start?

SAP Discovery Center: Your Gateway to AI-Powered Innovation

What is SAP Discovery Center?

Key Components of SAP Discovery Center

1. Discover

2. Evaluate

3. Adopt

Featured Content in SAP Discovery Center

Missions

Reference Architectures

SAP BTP Services

AI Features Catalog

The AI Feature Estimator

Related Resources

Why SAP Discovery Center Matters for AI Implementation

The Importance of Artificial Intelligence Training for SAP Solutions

Exploring the SAP Discovery Center: Strategic Positioning in Artificial Intelligence

List of Artificial Intelligence Training for SAP

Detecting Handwritten vs. Digital Docs in SAP CAP + DOX

SAP BDC Demo Environment

Need information on different SAP AI modules/Resources

Can Joule AI Make SAP Readiness Check & Process Insights Smarter?

SAP Business AI – Driving the Next Wave of Enterprise Innovation

Why It Matters

Error

Finding SAP Note using AI

The Challenge Every Basis Administrator Knows

Where AI Can Make a Difference

How It Could Work in Practice

Final Thoughts

SAP Ecosystem - AI Rank Tracking for Suppliers

Unable to Complete Practical Test- Applying the Value Story of SAP Business AI

How to properly configure SAP CAP MCP Server so Codex can detect it?

How can GenAI be effectively used in SAP S/4HANA implementation and support projects?

AI powered products - Alignment with SAP

Technical RFC: Building an "Auditable Autopilot" for Material Shortages – Where is the real Excel He

Question: Role-based access control in a chatbot using SAP Generative AI Hub

How AI Is Improving Clinical Communication and Compliance in Life Sciences