SAP Community - SAP AI Services

Release Navigator for SAP BTP What's New in Q4 2023 and What's Next in 2024

2024-02-05T15:50:27.237000+01:00

The release navigator for SAP Business Technology Platform by SAP Enterprise Support consolidates release information across SAP BTP products and services. You can find blog posts, newsletters, videos, podcasts, webcasts, social media channels, webpages, and more helpful content created by SAP Product Management, SAP User Assistance, SAP Learning, SAP Enterprise Support, and other areas of the SAP ecosystem in this easy-to-use web interface. It’s available to all customers as part of the SAP Enterprise Support offering.

You can now access the release navigator easier and with less clicks. Based on your feedback, we launched a new page on the SAP Support Portal that features a direct link to the release navigator for SAP BTP as well as to all other currently available release navigators.

The new Release Ready web page on the SAP Support portal

How to navigate to the Release Ready page in the SAP Support Portal:

In the SAP Support Portal, click on Offerings & Programs and select Release Ready.

This is where you can find the Release Ready page within the SAP Support Portal

Access via the SAP Enterprise Support Value Maps continues as previously. Additionally, you can now also find the release navigator within the Onboarding Resource Center.

We have published a quarterly maintenance update with the Q4 release cycle. You can find updated dates and resources according to the latest releases for the products featured.

Additionally, you can get to product pages with less clicks now due to a simplified navigation on the Home page of the release navigator.

Did you know that the release navigator is created using SAP Enable Now, which offers a machine translation feature? You can translate all text on the navigator pages automatically to 40 languages.

The translation feature is available in the authenticated version of the release navigator which requires sign in with an S-user, Universal ID or internal ID.

You can access the release navigator using a sign-in, by clicking on the tile "Translation Feature - Authenticated Version" in the section "About the Release Navigator". After you have signed in with one of the authorizations mentioned, you will be able to see the translation option at the top right corner of the screen.

Upcoming updates for the release navigator are scheduled on a quarterly basis in alignment with the Harmonized Release Calendar for SAP Cloud Products.
Content and UX updates to the BTP release navigator are planned for CW10, CW22, CW35 and CW48 of 2024. This schedule is subject to change without prior notice including, when necessary, delays or incorporating changes in subsequent update windows. It is not a commitment, promise, or legal obligation to deliver any material, code, or functionality.

Here’s a sneak peek at some of our Roadmap items for the release navigator:

Content Updates:

Updating resources relating to a specific quarterly release cycle
Integrating more products and services on dedicated product pages

UX Updates:

Continuous improvements of the UX elements for an easier navigation
Alignment of UX among the release navigators

Since launch of this tool in September 2023 we have received a great number of positive reactions and constructive feedback. Thank you and please keep the input coming, we appreciate all feedback!

Find all our teams blogs with the tag Readiness@Scale and feel free to connect here in the SAP Community or on LinkedIn.

Happy navigating!

Readiness@Scale
SAP Enterprise Support

Streamline Your Cash Flow with SAP Cash Application: Your Guide to Faster Payments & Efficiency

2024-02-14T04:45:48.463000+01:00

In today's fast-paced business environment, managing cash flow efficiently is crucial. Delays in receiving payments can disrupt operations, impact profitability, and hinder growth. Fortunately, innovative solutions like SAP Cash Application, powered by SAP Business AI, are revolutionizing the way businesses automate and optimize their accounts receivable processes.

What is SAP Cash Application?

Essentially, SAP Cash Application is a cloud-based software solution that leverages machine learning and automation to streamline the accounts receivable process. It automatically captures and extracts payment information from various sources, like emails and scanned documents, eliminating manual data entry and reducing errors. Additionally, it intelligently matches payments to invoices, automates reconciliations, and prioritizes collections based on predicted payment behavior.

Benefits of Using SAP Cash Application:

Reduce Days Sales Outstanding (DSO): Faster processing and matching of payments lead to quicker cash in your pocket, improving overall financial health.
Enhanced Efficiency: Automation eliminates manual tasks, freeing up valuable time and resources for your team to focus on strategic initiatives.
Improved Accuracy: Machine learning reduces human error in data entry and matching, leading to more accurate records and financial reporting.
Greater Transparency: Gain real-time insights into your cash flow and collection performance, enabling data-driven decisions.
Reduced Costs: Automation minimizes manual efforts and potential errors, lowering operational costs associated with accounts receivable management.

When to Use SAP Cash Application:

Businesses of all sizes can benefit from this solution, particularly those with high invoice volumes, diverse payment methods, or complex receivables processes.
Industries like manufacturing, retail, and healthcare often struggle with manual tasks and require efficient cash flow management, making SAP Cash Application a valuable tool.
Organizations seeking to improve DSO, enhance accuracy, and gain real-time financial insights can leverage this solution to achieve their goals.

How to Use SAP Cash Application:

SAP Cash Application features a user-friendly interface with intuitive workflows. Its core functionalities include:

Automatic capture and extraction of payment information: Documents are automatically categorized and relevant data extracted using machine learning.
Intelligent invoice matching: The system intelligently matches incoming payments to open invoices based on various criteria.
Automated reconciliations: Discrepancies are automatically flagged and reconciled, minimizing manual intervention.
Prioritization of collections: Based on predicted payment behavior, the system prioritizes collections efforts for faster cash inflow.
Real-time dashboards and reports: Gain instant insights into your cash flow, collections performance, and key metrics.

In Conclusion:

SAP Cash Application, powered by SAP Business AI, is a powerful tool that can transform your accounts receivable process. By automating tasks, reducing errors, and providing real-time insights, it helps businesses improve efficiency, accelerate cash flow, and make data-driven decisions.

You should also refer to SAP Help Portal to gain more information about SAP Cash Application, be it about the pre-requisites, how to set it up etc.

#SAPBusinessAI #SAPAI #SAPCashApplication

Navigating the AI Landscape: A UX Designer's Guide

2024-02-14T11:01:22.387000+01:00

Introduction

As we stand on the brink of a technological revolution, AI is reshaping the way we interact with the digital world. I'm here to share my insights regarding this evolving landscape, offering observations into how AI, machine learning, and other related technologies can enhance our work in UX design.

Brief Overview of AI and ML

Artificial Intelligence (AI) is the overarching concept of machines carrying out tasks in ways that we consider 'smart'. It's a broad discipline aimed at creating systems that can simulate various aspects of human intelligence. Machine Learning (ML) is a subset of AI, focused on algorithms that enable machines to improve at tasks with experience. Think of AI as the universe of intelligent computation, with ML being a planet within it.

Deep Learning, in turn, is a subset of ML. It involves neural networks with many layers (hence 'deep') that can learn and make intelligent decisions on their own. Deep Learning has been instrumental in achieving significant breakthroughs in areas like image and speech recognition.

Then come Foundation Models, a recent development in AI. These are large-scale models (like GPT-3) that are trained on vast amounts of data and can be adapted to a wide range of tasks without being specifically trained for them. They are called 'foundations' because they provide a base layer of understanding that can be built upon for various applications.

These technologies are interrelated, forming a hierarchy from broad to specific. AI encompasses everything intelligent that a machine might do. Under AI, ML is the method through which machines learn from data. Deep Learning is a further specialization of ML with a focus on complex, layered neural networks. And Foundation Models are the cutting-edge, versatile systems pushing the boundaries of what AI can achieve.

Generative Pretrained Transformer (GPT) and Transformer Architecture

GPT, or Generative Pretrained Transformer, is a type of language processing AI. It's built on the Transformer architecture, which is revolutionary in the way it handles data. Traditional models processed data sequentially, one piece after another. The Transformer, however, allows for parallel processing, looking at entire sequences of data at once. This is done through mechanisms called 'attention' and 'self-attention,' letting the model weigh the importance of different parts of the input data and learn the context more effectively.

GPT takes this architecture and applies it to language, learning to predict the next word in a sentence. It's trained on a vast corpus of text and then fine-tuned for specific tasks. This pretraining is what makes it 'generative' - it can generate text, not just understand or classify it.

Inception of Generative AI

Generative AI came into existence as a natural progression from earlier AI models that were primarily discriminative. While discriminative models could classify and understand data, they couldn't create new data. The inception of Generative AI marked a shift from understanding to creation.

This shift was fueled by advancements in neural networks and an exponential increase in computational power and data availability. Researchers began exploring how neural networks could not only recognize patterns but also use those patterns to generate new, similar data. This exploration led to the development of models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), and eventually to large-scale models like GPT.

Integrating Reinforcement Learning with Human Feedback

Adding to this is the concept of Reinforcement Learning with Human Feedback (RLHF). This approach trains AI systems not just on static datasets but on dynamic feedback loops. It’s akin to teaching a pet new tricks: the AI tries different strategies and, based on human feedback, learns which actions are desirable or undesirable. This human-in-the-loop methodology ensures that the AI’s learning trajectory aligns more closely with human values and preferences, making it particularly potent for personalization and adaptive learning scenarios.

Incorporating RLHF into the design of AI systems compels us to consider not just the initial user interaction but the ongoing relationship between the user and the AI as they adapt and learn from each other over time.

Various Approaches to AI

Understanding the various approaches to AI can be daunting, but it's crucial for harnessing its full potential:

Symbolic AI: Based on the manipulation of symbols and rules, this approach tries to mimic human reasoning.
Probabilistic AI: Focuses on using probabilities to make predictions and decisions.
Statistical AI: Uses statistical methods to infer patterns and make predictions.
Large Language Models (LLMs): These are trained on vast datasets to understand and generate human-like text.

Each approach has its strengths, and knowing which to apply can significantly impact the success of a project.

How Are Models Trained?

AI models are trained using large datasets. They learn by recognising patterns and making associations. For instance, an image recognition model might learn to identify cats by being shown thousands of pictures of cats and not-cats. Over time, it improves its accuracy through a process called backpropagation, where it adjusts its internal parameters to minimize errors.

AI and Design

In the realm of AI, as designers, we're tasked with shaping a human-machine relationship that is continually evolving. Unlike traditional design, where interactions are fixed, AI-based design involves a fluid, ever-changing dynamic. Non-AI systems interact in a consistent, unchanging manner. However, AI systems learn and adapt over time, leading to a constantly developing relationship.

As both the machine and humans learn and adapt, they engage in a reciprocal learning process, forming a dynamic feedback loop. This loop is characterized by an ongoing exchange of information, with both parties growing and adjusting to the interaction. Our role as AI designers is to foster effective communication within this vibrant human-machine relationship.

In this context, data becomes the pivotal element. Previously, data interaction was straightforward — a user command followed by a machine response. With AI, data is the foundation of learned behaviours, informing the machine and shaping its growth. Here, data inputs are about educating the machine with information, not just instructing it. Conversely, machine outputs are no longer fixed; they have generated responses that necessitate explanations of how the data led to a particular conclusion.

As designers, we are charged with orchestrating these interactions throughout the entire data lifecycle. This includes the initial data capture, guiding the machine's learning process, designing clear and understandable data outputs, and ensuring a seamless flow of information back into the system for continued learning. Our responsibility extends beyond creating interfaces; it's about crafting experiences that support this rich, ongoing dialogue between humans and AI, ensuring clarity, transparency, and a mutual growth trajectory.

A comparison can be drawn from the days when selecting a movie at a DVD store involved browsing aisles for a favoured title or relying on a recommendation from a salesperson. In contrast, platforms like Netflix now curate suggestions tailored to our tastes, informed by our viewing history.

Designers find themselves at the intersection of innovation, with three distinct yet interconnected realms to explore:

1. Designing with AI: Crafting Alongside AI

Designers are now partnering with AI in a collaborative dance of creation, where the output is a fusion of human ingenuity and machine efficiency. Imagine the synergy of a designer working with AI to conjure up ground-breaking architectural structures, much like Autodesk's venture in utilizing generative design principles to conceive their Toronto office. Here, AI becomes an ally in the creative process, providing new perspectives and solutions that push the boundaries of traditional design.

Moreover, designers harness AI to streamline repetitive tasks, freeing up creative energy for more complex challenges. Tools like Airbnb's system for transforming sketches into digital wireframes or Netflix's algorithm for adapting graphics across different cultures exemplify this trend. These innovations signify a new era where AI does not replace the designer but rather amplifies their capabilities.

2. Designing for AI: The Human-Centric Design Approach

Designing for AI requires a human-centred lens, focusing on crafting systems that prioritize user needs and experiences. It's about spotting those unique opportunities where AI can not only function but flourish in addressing real-world problems. Here, the designer's role transcends aesthetics, venturing into the realm of functionality and utility, transforming user needs into data-driven AI solutions.

Human-centered design makes AI effective. These are some ways:

1. Beyond Interfaces: Embracing Human-Centered Design in AI Algorithm Development

UX can aid in designing algorithms that mirror the decision-making processes humans employ, by considering their information, goals, and constraints. It can ensure that the decision environment, which encompasses both the algorithm and its human users should be thoughtfully constructed. Users should comprehend their AI tools well enough to use them effectively. Designers also aid in establishing guidelines and business protocols that translate algorithmic predictions into actionable insights, advising when human intervention is appropriate to supplement or override the AI.

2. To Translate User Needs into Data Requirements: A designer can aid in identifying the type of data necessary for training the model, considering various factors like predictive power (A percentage that refers to an ML model’s ability to predict outcomes given a certain input correctly), relevance, fairness, privacy, and security. Ensure the training dataset is comprehensive, reflecting the real-world scenarios the AI will encounter, and free from biases.

3. Knowing the source of the data & Tuning the Model: Evaluating data sourcing and collection methods for their suitability of the project is critical. Once deployed, A designer will assess if the AI meets the target user’s needs as per predefined success metrics. Provide feedback on adjusting the model’s parameters as needed to enhance its performance, focusing on metrics that reflect user experience, such as customer satisfaction or the frequency of users following the AI’s recommendations.

4. Addressing Bias, Fairness, and Transparency: UX designers can help analyze data with an understanding of the domain, Goal definition, possible and required outcomes and the process that generated it. This leads to designers being a crucial part of designing algorithms that are mindful of the environment they will operate in, avoiding controversial predictors. They aid in conducting usability tests or audits to detect and eliminate unintended biases.

5. Managing the Handoff in AI Systems: Designing for smooth transitions between AI and human control in situations demanding common sense or contextual understanding is very important. The accountability for any action in the real world still lies with human users as over-reliance on technology can leave users unprepared for instances where AI fails, necessitating more skilled human intervention. A UX designer can analyse such situations and will aid in designing smooth handoff processes.

6. Designing Reward Functions and leveraging User Feedback for Model Improvement: Designing the AI’s reward function is critical as it influences the user experience significantly. User feedback is essential in refining AI models and enhancing user experience. Designers analyse and optimize the reward data to enhance the model for long-term user benefits and anticipate the downstream effects of your product. This also allows users to contribute to the personalization of their experiences, thereby increasing their trust in the system.

7. Anticipating Errors and Designing Response Pathways: A Human-centred design prepares your AI system to facilitate user responses to inevitable errors, turning them into opportunities for learning and improvement.

8. Educating Users and Setting Realistic Expectations: Designers help communicate the capabilities and limitations of your AI product to customers. Help users develop accurate mental models and understand how their interactions train the system. It’s essential to balance user trust, avoiding both undue scepticism and over-reliance on AI.

9. Guiding User Trust in AI Systems: Users need to adjust their trust in AI systems appropriately, rather than relying on them implicitly in every situation. The phenomenon of 'algorithm aversion' is well-documented, where users may be sceptical of software systems. Conversely, there are instances where users place excessive trust in AI capabilities, expecting more than what the system can deliver. Designers help users develop a balanced level of trust, aligning with the system's actual capabilities and limitations by taking a human-centred approach.

For instance, openly acknowledging the potential inaccuracies in AI predictions can temporarily reduce trust in those specific outcomes. However, this honesty can foster a more sustainable trust in the long term. Users become more judicious in their reliance on the system, reducing the likelihood of disappointment due to misplaced expectations."

3. Designing of AI: The User Experience Frontier

When it comes to the design of AI, it's about envisioning and sculpting the interactions between AI systems and their human users. It's a space where new forms of engagement, like voice-activated assistants or image recognition software, become gateways to enhanced user experiences. The key challenge here is transparency: designing interfaces that not only serve but also educate. Users should be able to grasp, with just the right level of detail, how AI systems make decisions and learn over time.

An example could be the intuitive dashboards in our cars that provide real-time insights into the vehicle's AI, or the smart home devices that learn our preferences and conversationally explain their actions. These are no longer scenarios from a sci-fi novel; they are today's design challenges that call for a blend of technical knowledge, user empathy, and creative foresight.

In essence, the designer's canvas has expanded, not just in size but in dimensionality. As AI continues to intertwine with our daily lives, it invites designers to step into roles that are as diverse as they are dynamic, shaping not only how AI looks but also how it behaves and interacts in the fabric of human experience.

Understanding the Limitations of AI in UX Design

Incorporating AI into UX design comes with distinct limitations that underscore the irreplaceable value of human insight and direction.

1. Hallucinations and Reliability

AI "hallucinations" refer to instances where a model confidently generates an incorrect response. These can be caused by inconsistencies within a large data set or errors in the model's training methodology. In fields where precision is critical, such as financial reporting or legal documentation, these inaccuracies can introduce significant risks. Combatting this requires robust document structuring and advanced prompt design techniques to direct AI towards more dependable outcomes.

2. Prompt Sensitivity

Large Language Models (LLMs) are highly sensitive to user input. The nuances of how a prompt is phrased can lead to varied and unpredictable responses. This sensitivity necessitates a careful and strategic approach to prompt engineering, ensuring that the AI's responses align with user intentions. The evolving role of "Prompt Engineer" is a testament to the significance of crafting prompts that steer AI toward delivering consistent and accurate results.

3. Context Window

Limits The context window, the amount of information an AI can consider when generating a response, is a notable constraint. As the context window expands, so does the computational complexity. Despite improvements like GPT-4's extended context window, there remains a ceiling to the volume of data an AI can process at a time. This limitation is particularly challenging in tasks that require the review of extensive documents, where the AI must understand and analyze large quantities of text. Designing for AI in UX thus requires a thoughtful balance between the AI's capabilities and the complexity of the tasks it is expected to perform.

Things Designers Should Know Before Designing for AI

Technical Knowledge

Grasping the basics of data science and AI techniques like NLP and deep learning.
Basic Understanding of the AI toolchain and DevOps processes for AI development.
Access to clean data set.

Ethics in AI

Integrate ethical standards into the design and development process, ensuring accountability, fairness, and transparency.
Navigate GDPR/compliance mandates and be mindful of the financial and human impacts of design decisions.

Collaboration is Key:

Work closely with data scientists and engineers to understand the technical aspects and constraints.
Foster a shared vision across multidisciplinary teams and participate actively in the AI development lifecycle.
Engage in design thinking activities tailored to AI and address the needs of diverse users.

AI Strategy

Develop and communicate a user-focused AI strategy, explaining barriers to adoption and the business and user benefits.
Articulate your company's AI strategy, differentiators, and the journey toward AI success.

Designing AI Interactions

Translate AI model outputs into understandable insights for users and design interactions that capture human input for machine learning.
Prototype and test AI solutions frequently, and design for various stages of the AI lifecycle, such as model maintenance and data collection methods.

User Trust and Transparency

Build trust by making your AI interactions transparent and understandable.

In conclusion, AI is not just a tool but a new frontier in design. By embracing it, we can create more personalized, efficient, and engaging user experiences. Let's embark on this journey together, continuously learning and adapting to ensure technology serves humanity in the most beneficial ways.

Resources to Refer :

Generative AI at SAP: https://open.sap.com/courses/genai1
AI Ethics at SAP: https://open.sap.com/courses/aie1-1
Designing for Generative AI: https://experience.sap.com/internal/fiori-design-web/generative-ai-design/
Designing for Intelligent Systems: https://experience.sap.com/internal/fiori-design-web/designing-intelligent-systems/
SAP AI community file: https://www.figma.com/file/SJh2Eb5KrBIbaK3Bo2wqtq/AI-UX-Pattern-Community-Explorations?type=design&node-id=737-200772&mode=design
AI Design Community Teams Channel
AI Design Community Exploration File
Prompt Pattern Presentation Figjam

Supercharge Your SAP HANA Cloud Experience with SAP EarlyWatch Alert

2024-02-16T14:32:36.174000+01:00

The SAP EarlyWatch Alert service has been checking the administrative areas of SAP HANA for quite some time. This service, which includes numerous dashboards, forecasts, and notifications in SAP for Me, now also supports SAP HANA Cloud databases on SAP BTP. All productive cloud tenants are automatically connected via SAP HANA Cloud Statistics Service to the SAP EarlyWatch Alert, and the collection starts in February 2024.

Among the five options available to collect and transfer data to SAP, SAP HANA Cloud Statistics Service stands out as the newest, as depicted in the screenshot below. The other options include SAP Solution Manager, SAP Focused Run, SAP HANA Cockpit, and SAP NetWeaver ABAP. To view the connected SAP HANA Cloud production systems, you can use the link provided to access the SAP EarlyWatch Alert Status app.

The content is already very comprehensive and will continue to expand throughout this year. Some key features include:

Largest Tables with growth history and prediction
E-Mail notification on 2 Billion Record Predictions (requires at least a six-weeks history)
Memory Consumption with long-term statistics and drill-down capabilities
Memory Prediction and notifications on out-of-memory predictions
Disk size history
CPU consumption with long-term statistics
Delta Merge Performance and Errors
SQL Analysis with hint recommendation: Open Results

Note: The prerequisites for using the service are the"Service Reports and Feedback" and "Manage Alert(s) in SAP EarlyWatch Alert for all S-users" authorizations in SAP for Me which must be assigned by your company's user administrator. Use this link to request them: SAP for Me authorization.

There are four different apps in SAP for Me that you can use to review the content. Here is the best practice guide on how to use them:

Initial Set-up:

1. Open SAP EarlyWatch Alert Workspace and filter for your SAP HANA Cloud systems using the product filter. Feel free to add more filters which narrow down the landscape you are responsible for.

2. Save the filters under a new view, for example, "My HANA Cloud Systems"

3. Click the "Manage notification" button and subscribe to all notifications topics. Alternatively: Use this link for one-click subscription. Or see this blog on how-to subscribe to email notifications in SAP for Me for SAP EarlyWatch Alert Workspace.

4. Create a reoccurring weekly calendar entry with the link to the App Solution Finder

Regular Tasks:

1. Open Solution Finder once a week and check for new alerts (Link pre-filtered to HANA Cloud)

2. Hide or snooze any alerts which are not relevant or where the recommended action is already being implemented

3. Open SAP EarlyWatch Alert Workspace to review the forecasts for table growth and memory consumption

4. Open SAP EarlyWatch Alert Dashboard to access long-term consumption statistics: Be sure to have your system number ready to identify your HANA.

5. Optional: If you are prefer reading full reports: Open the SAP EarlyWatch Alert Reports app to review the complete report.

The SAP EarlyWatch Alert Dashboard offers a comprehensive overview of hardware consumption, as depicted in the screenshot below. To delve deeper into the weekly or hourly statistics, simply click on the chart icon located in the upper right corner of each chart. This feature enables users to analyze data in a more granular manner, facilitating a better understanding of resource utilization.

In the header section of the dashboard, you will find useful links to the full report, the SQL Analysis and the alerts.

In addition to the statistics provided by the SAP HANA Cloud alerts that are integrated into the product, the SAP EarlyWatch Alert service offers more advanced alerts created by the SAP EarlyWatch Alert experts. These alerts can be updated independently of the SAP HANA Cloud release cycle in our expert system. Below is a sample list of alerts that can be identified by the expert system algorithms behind the SAP EarlyWatch Alert service:

Long blocking savepoint phase
High memory consumption for tables
Missing Hint causing expensive SQL statement
Large Delta Stores indicating Merge Issue

The SAP EarlyWatch Alert aims to be your Analysis Companion in SAP for Me supercharging your SAP HANA Cloud experience! Hope you like it.

SAP Business AI : Infuse AI in applications using SAP BTP (with some Use Cases)

2024-02-18T04:43:44.423000+01:00

Introduction

The primary focus of this article is to lucidly present a broad classification of SAP Business AI, followed by an in-depth explanation on how to infuse AI into applications using SAP Business Technology Platform (BTP). The spotlight will be on the AI/ML services within SAP BTP.

The article features some use cases drawn from my own projects. These examples are intended to illustrate the practical application of various technologies within SAP BTP, showing how they can be synergistically utilized to create end-to-end applications that are infused with AI.

Agenda

Broad classification of SAP Business AI & Introduction to various Services.
SAP BTP AI : AI services & solutions in SAP BTP to build applications infused with AI.
Example Use Cases
- Semantic Search App & RAG (Retrieval Augmented Generation example).
- Zero shot classification with LLM's
- AI Quiz App using LLM's

Broad classification of SAP Business AI

SAP Business AI can be broadly classified into following categories :

Embedded AI : AI Integrated into business processes and solutions. SAP business AI includes AI solutions & features infused in S4HANA (Finance , Sales & Service), ARIBA( procurement), SAP Digital Assistant for Cx ( marketing & commerce) , SAP Success Factors(HR), SAP Transport Management (Supply Chain), SAP Extended Warehouse Management (Supply Chain) and Cross Functional use cases with "Just Ask" at SAP Analytics cloud and many more.
SAP BTP AI : Build custom AI solutions and integrate them to your SAP landscape. BTP AI solutions Infuse artificial intelligence into your apps with scalability and responsibility in mind by enabling consumption of ready-to-use AI Models , training & deployment of custom AI models, using Enterprise Process Automation, SAP's Foundation Models, AI through SAP Partnerships : IBM Watson, Google Cloud, Data Robot, Microsoft OpenAI etc. SAP combines the power of generative AI with the context of business data and business processes and applies fine-tuning and prompt engineering at scale.
SAP AI Services : Models Pretrained on Business Data, access pretrained AI capabilities e.g. Document information extraction, Personalized recommendation and Data attribute recommendation.
Ecosystem AI : Procure best-of-breed AI solutions from SAP's ecosystem.

SAP BTP AI

We will now focus on SAP BTP AI foundational platform & services. Let's look at the foundational platform and services available to customers & partners today to infuse AI into applications.

SAP AI Services

Access Pretrained models as APIs for Document Information Extraction (Dox), Personalized Recommendation services, Data Attribute recommendations & Machine translation services etc. These pretrained models from SAP are trained on Business data and can be infused in BTP Applications to perform task like extracting Text from Images using OCR, retrieving master data from Invoices (e.g. pdf etc) or building custom recommendation services or machine translation services.

How can this help partners and customers to build AI applications ?

Partners and customers can consume SAP AI services API for DoX & recommendation services etc in custom BTP apps ( No code/ Low code apps plus Pro code apps) , to create side by side AI applications or extentions to existing exterprise applications.

Document Information Extraction Video

Documentation

SAP BTP Generative AI hub

SAP BTP Generative AI hub is available on SAP AI Launchpad/ SAP AI Core on BTP and provides a central place for accessing Large Language models from different providers such as GPT-4 by Azure OpenAI or OpenSource Falcon-40b .

How can this help partners and customers to build AI applications ?

The generative AI hub provides tooling for prompt engineering, experimentation, and other capabilities to accelerate the development of BTP applications infused with generative AI, in a secure and trusted way.
Partners and customers can build AI Applications on BTP using the Gen AI hub based access to LLM models as an API from SAP AI Core – Reference Architecture

Generative AI Hub – Out now! : Includes a Demo of Gen AI Hub.

SAP HANA Cloud Vector Engine (GA)

The world of Gen AI / LLM’s has lot of mention on vector data (embeddings) , which is simply put numerical representation of text, voice, Image data etc. With this vector representation of text, voice & images, use cases like similarity search or contextual search or Retrieval Augment Generation (RAG) are prevalent, wherein similar documents matching a user query are pulled from a vector data store , before being fed to a LLM for a contextual response.

Such application development can now happen in BTP for enterprise applications using the Reference architecture above which includes Gen AI Hub, HANA Vector Engine etc.

Video Vector Engine

Below is a 2D representation of embeddings generated on simple text like dog, cat, kitten, man,woman and SAP BTP etc. Words similar in meaning are clustered together.

I have created a Google colab notebook for basic understanding of these NLP terms including :

Transformers & self Attention mechanism
Word Embeddings
Similarity search
Retrieval Augmented Generation (RAG)
Interaction with PDF documents

SAP BTP AI Core/ AI Launchpad

This is the foundational technology stack for orchestrating and deploying custom AI/ML applications on BTP. AI Core/Launchpad allows you to incorporate your GIT Code repository along with config.yaml file containing the templatized parameters for your ML/AI scenarios, Docker secret & docker image, AI core resource to use (CPU/GPU config), any input parameters etc. AI core provides an SDK to interact with the foundation where as AI launchpad provides a UI5 Frontend to configure your ML/AI scenario. You can configure, deploy & Monitor your scenarios on AI Core & AI launchpad.

Example Application : Reduce your CO2 footprint using a smart Generative AI application on SAP BTP

SAP Analytics Cloud "Just Ask" & SAP Datasphere

Just Ask(GA Q1 2024) will provide a Natural Language processing AI interface within SAP Analytics cloud allowing the ability to interact with your data models & dashboards using Natural language based queries.

Video Demo

SAP Datasphere (SAAS Datawarehouse) allows federated data access from many systems allowing Partners to use data from SAP Business applications ( S/4, Success Factors, Ariba etc) or Non SAP Business applications with other datasets on varied on premise/cloud environements to produce powerful Business reporting applications in SAP Analytics cloud, which now also provides inbuilt Gen AI capability with Just Ask.

Below discovery mission includes details on how customers & partners can use data from AWS, Azure, and Google Cloud with the federation capabilities of SAP Datasphere.

Discovery Mission : Explore your Hyperscaler data with SAP Datasphere

Video : SAP Datasphere

Try & Experience SAP Datasphere

SAP Build Code

Build Code will provide “Joule” assistant for faster application development by generating code, data models & by providing Guided experience for developers.

Example Use Cases

Semantic search app

Following is a sample search application deployed on Cloud foundry using Python Build pack. A csv of all documents uploaded to sharepoint (or sap workzone) was extracted (typically using the "Reports" section which extracts the list of docs, url, num of clicks, Title, decription etc). This is then processed in python and the Text from Title & description is merged and tokenized. The tokenized text for all docs is then converted into a TF-IDF Matrix (for Term based search which is the default option in below app screen) and also converted to Vector Embeddings using Google BERT model for contextual search.

When a user puts in a query its processed to removed white spaces , special characters etc, tokenized and then compared with the existing Matrices for TF-IDF or Embeddings using Cosine Similarity to retrieve the top matching documents.

The missing piece in the architecture below is a Vector database to store the text embeddings as a persistent layer. I have used a FAISS in memory index which is loaded in memory when the app starts up. For full fledged guidance from SAP on such architectures refer the Reference Architecture.

To understand what contextual search means here let's look at an example, Let's say a user searches for "Aviation Industry relared demo & presentation content" , the results will include all documents which are close in the embedding space example Demos related to Airlines, Flights , Airport Management, Airport ground operation management etc.

BTP Solution Components

AI core / BTP CF Runtime python buildpack(Python API), docker image

AI Launchpad : ML Ops , orchestration and monitoring of deployed API.

SAP Build Apps : Frontend

SAP BTP Object Store : CSV extracts store

HANA Cloud Vector database : Text embeddings / similarity search

Production Architecture with RAG

In this reference architecture pattern, learn how to seamlessly combine various Large Language Models (LLMs) using SAP AI Core. Maximize the potential of LangChain in CAP model and implement advanced methods such as custom schema-based output parsing or Retrieval Augmented Generation (RAG) with embeddings and a vector database to further enhance the benefits for your specific needs. This reference architecture accommodates both Cloud Foundry and Kyma runtimes, providing adaptability in your endeavor to leverage GenAI on SAP BTP.

Retrieval Augmented Generation with GenAI on SAP BTP

Zero Shot Classification using LLM

Following is another example wherein I tried Zero Shot classification using a GPT model orchestrated using SAP Gen AI Hub ( SAP AI Core/Launchpad)

Here users are using SAP Analytics cloud to visualize the Average Co2 on a dashboard based on an analysis of their spend at various vendors/retails and across various transaction descriptions. As there are too many descriptions the dashboard is not very intuitive, we then call AI Core GPT proxy to categorize transactions to fixed Categories like Retail,Accomodation, Food etc. using a prompt like below :

PROMPT

Here we are asking the GPT model to assign close to 66 transaction descriptions into 13 categories, the Index of the data will be preserved and will be later used to create the mapping in the data model.

Categorize the numerized descriptions below into the 13 categories ['Agricultural Services' 'Contracted Services' 'Airlines' 'Car Rental'
 'Lodging' 'Transportation Services' 'Utility Services'
 'Retail Outlet Services' 'Clothing Stores' 'Miscellaneous Stores'
 'Business Services' 'Professional Services and Membership Organizations'
 'Government Services'].

Display the result in a table with the format "| INDEX OF DESCRIPTION | CATEGORY |".
Descriptions:
| 0 | Meat Provisioners – Freezer and Locker |
| 1 | Grocery Stores, Supermarkets |
| 2 | Cable and other pay television (previously Cable Services) |
| 3 | Digital Goods: Media, Books, Movies, Music |
| 4 | Financial Institutions – Manual Cash Disbursements |
| 5 | Sporting Goods Stores |
| 6 | Service Stations ( with or without ancillary services) |
| 7 | Financial Institutions – Merchandise and Services |
| 8 | Eating places and Restaurants |
| 9 | Household Appliance Stores |
| 10 | Drugs, Drug Proprietors, and Druggist’s Sundries |
| 11 | Glass, Paint, and Wallpaper Stores |
| 12 | Petroleum and Petroleum Products |
| 13 | Taxicabs and Limousines |
| 14 | Book Stores |
| 15 | Drug Stores and Pharmacies |
| 16 | Candy, Nut, and Confectionery Stores |
| 17 | Veterinary Services |
| 18 | Miscellaneous and Specialty Retail Stores |
| 19 | Money Orders – Wire Transfer |
| 20 | Shoe Stores |
| 21 | Lodging – Hotels, Motels, Resorts, Central Reservation Services (not elsewhere classified) |
| 22 | Family Clothing Stores |
| 23 | Miscellaneous Home Furnishing Specialty Stores |
| 24 | Fast Food Restaurants |
| 25 | Caterers |
| 26 | Local/Suburban Commuter Passenger Transportation – Railroads, Feries, Local Water Transportation. |
| 27 | Misc. Food Stores – Convenience Stores and Specialty Markets |
| 28 | Legal Services and Attorneys |
| 29 | Men’s Women’s and Children’s Uniforms and Commercial Clothing |
| 30 | Civic, Fraternal, and Social Associations |
| 31 | Department Stores |
| 32 | Orthopedic Goods Prosthetic Devices |
| 33 | Telecommunications Equipment including telephone sales |
| 34 | Nurseries – Lawn and Garden Supply Store |
| 35 | Package Stores – Beer, Wine, and Liquor |
| 36 | Men’s and Women’s Clothing Stores |
| 37 | Computers, Computer Peripheral Equipment, Software |
| 38 | Dairy Products Stores |
| 39 | Membership Organizations ( Not Elsewhere Classified) |
| 40 | Equipment Rental and Leasing Services, Tool Rental, Furniture Rental, and Appliance Rental |
| 41 | Watch, Clock, Jewelry, and Silverware Stores |
| 42 | Travel Agencies and Tour Operations |
| 43 | Laundry, Cleaning, and Garment Services |
| 44 | Commercial Footwear |
| 45 | Card Shops, Gift, Novelty, and Souvenir Shops |
| 46 | Digital Goods: Games |
| 47 | Hardware Stores |
| 48 | Cigar Stores and Stands |
| 49 | Bakeries |
| 50 | Membership Clubs (Sports, Recreation, Athletic), Country Clubs, and Private Golf Courses |
| 51 | Recreation Services (Not Elsewhere Classified) |
| 52 | Women’s Ready-to-Wear Stores |
| 53 | Women’s Accessory and Specialty Shops |
| 54 | Tax Payments |
| 55 | Management, Consulting, and Public Relations Services |
| 56 | Drinking Places (Alcoholic Beverages), Bars, Taverns, Cocktail lounges, Nightclubs and Discotheques |
| 57 | News Dealers and Newsstands |
| 58 | Ambulance Services |
| 59 | Industrial Supplies, Not Elsewhere Classified |
| 60 | Computer Software Stores |
| 61 | Motion Picture Theaters |
| 62 | Cosmetic Stores |
| 63 | Electronic Sales |
| 64 | Hospitals |
| 65 | Miscellaneous Publishing and Printing |
| 66 | Wholesale Clubs |

RESULTS:

As you see in the results 66 Indexes are now assigned to 13 Categories or transaction descriptions:

This classification is based on the provided categories and the details of the descriptions:

| INDEX OF DESCRIPTION | CATEGORY |
|----------------------|-------------------------------------|
| 0 | Retail Outlet Services |
| 1 | Retail Outlet Services |
| 2 | Utility Services |
| 3 | Retail Outlet Services |
| 4 | Financial Institutions |
| 5 | Retail Outlet Services |
| 6 | Retail Outlet Services |
| 7 | Business Services |
| 8 | Retail Outlet Services |
| 9 | Retail Outlet Services |
| 10 | Retail Outlet Services |
| 11 | Retail Outlet Services |
| 12 | Retail Outlet Services |
| 13 | Transportation Services |
| 14 | Retail Outlet Services |
| 15 | Retail Outlet Services |
| 16 | Retail Outlet Services |
| 17 | Professional Services and Membership Organizations |
| 18 | Miscellaneous Stores |
| 19 | Financial Institutions |
| 20 | Clothing Stores |
| 21 | Lodging |
| 22 | Clothing Stores |
| 23 | Retail Outlet Services |
| 24 | Retail Outlet Services |
| 25 | Retail Outlet Services |
| 26 | Transportation Services |
| 27 | Retail Outlet Services |
| 28 | Professional Services and Membership Organizations |
| 29 | Clothing Stores |
| 30 | Professional Services and Membership Organizations |
| 31 | Retail Outlet Services |
| 32 | Retail Outlet Services |
| 33 | Retail Outlet Services |
| 34 | Agricultural Services |
| 35 | Retail Outlet Services |
| 36 | Clothing Stores |
| 37 | Retail Outlet Services |
| 38 | Retail Outlet Services |
| 39 | Professional Services and Membership Organizations |
| 40 | Contracted Services |
| 41 | Retail Outlet Services |
| 42 | Professional Services and Membership Organizations |
| 43 | Professional Services and Membership Organizations |
| 44 | Clothing Stores |
| 45 | Retail Outlet Services |
| 46 | Retail Outlet Services |
| 47 | Retail Outlet Services |
| 48 | Retail Outlet Services |
| 49 | Retail Outlet Services |
| 50 | Professional Services and Membership Organizations |
| 51 | Recreation Services |
| 52 | Clothing Stores |
| 53 | Clothing Stores |
| 54 | Government Services |
| 55 | Professional Services and Membership Organizations |
| 56 | Retail Outlet Services |
| 57 | Retail Outlet Services |
| 58 | Professional Services and Membership Organizations |
| 59 | Retail Outlet Services |
| 60 | Retail Outlet Services |
| 61 | Recreation Services |
| 62 | Retail Outlet Services |
| 63 | Retail Outlet Services |
| 64 | Professional Services and Membership Organizations |
| 65 | Retail Outlet Services |
| 66 | Retail Outlet Services |

Note: There were some descriptions that didn't fit perfectly into any of the provided categories. For these, the closest match was chosen based on the context. For example, "Financial Institutions – Manual Cash Disbursements" and "Financial Institutions – Merchandise and Services" were assigned to "Financial Institutions", which is not present in the provided categories but seems the most appropriate based on the description.

The AI Core code calls GPT to map the transaction descriptions to the outcome categories & the results are then processed and saved back in the HANA cloud database. The SAC live connection then pulls the updated records in a new simplified dashboard.

Below is a simpler dashboard with lesser categories after update from GPT :

BTP Solution Components

AI core & Gen AI Hub : App logic in Python in a docker image(GPT call / prompt management)

AI Launchpad : ML Ops , operations monitoring of deployed API.

SAP Analytics Cloud : Frontend

AI QUIZ APP

Following is a sample quiz application which uses Gen AI to create Multiple Choice Question & Answers , using product specific or organization specific scenarios. Quizes can be generated in JSON format using a LLM and then served using a Frontend application.

PROMPT

In the prompt we have provided a JSON template for the response, GPT model will return question/answer pair in this template , making it easier to directly use this in the frontend application.

using the below text as context : 

"
What Is Edge Integration Cell
Edge Integration Cell is an optional hybrid integration runtime offered as part of SAP Integration Suite, which enables you to manage APIs and run integration scenarios within your private landscape.
The hybrid deployment model of Edge Integration Cell enables you to:
* Design and monitor your integration content in the cloud.
* Deploy and run your integration content in your private landscape.
To learn more about how hybrid deployment works using Edge Integration Cell, see Hybrid Deployment Using Edge Integration Cell.
To compare hybrid deployment with the standard deployment model of SAP Integration Suite, see Standard Deployment in the Cloud.
Edge Integration Cell supports the following use cases:
* Security or compliance use cases In many enterprises, sensitive data must be managed and controlled inside the enterprise's firewall. In addition, there could be strict architectural restrictions and you want to keep your data within your private landscape.
* Getting a migration path for SAP Process Integration customers SAP Process Integration is used by many customers for ground-to-ground integration scenarios. Edge Integration Cell allows these customers to benefit from the newest innovations that come with SAP Integration Suite, with the option to design and monitor integration content in the cloud, and deploy and run their scenarios exclusively in their private landscapes. Note If you want to use integration flows to integrate your on-premise applications, you can use SAP Process Orchestration in addition to SAP Integration Suite. SAP Integration Suite comes with a set of runtime profiles that make sure that the integration flow editor allows you to design only those features that are supported by the runtime components of a specific SAP Process Orchestration release. For more information, see Runtime Profiles. However, this option requires you to install a separate product, SAP Process Orchestration, for processing and monitoring messages. Using Edge Integration Cell, you can manage all tasks – from integration design up to processing and monitoring the scenario – within one service: SAP Integration Suite. 
In both cases, we recommend using a hybrid integration approach for enterprise-wide connectivity.
For more information about the supported features and limitations of Edge Integration Cell, see Edge Integration Cell Runtime Scope.
"

Generate five multiple choice question answer set output as a single JSON code, using below template , the link property of the JSON should default to the url: “link":"https://help.sap.com/docs/integration-suite/sap-integration-suite/what-is-sap-integration-suite-edge-integration-cell”

{
            "type": "multiple",
            "difficulty": "easy",
            "category": “SAP BTP - Edge”,
            "question": “What is fundamental technology behind ChatGPT”,
            "correct_answer": “Generative AI”,
            "incorrect_answers": [
                "Its cool",
                "Ethanol",
                "Formaldehyde"
            ],
            "link":""

Test the Response using different LLM models on Gen AI Hub , here we have the opportunity to maintain our prompts.

The generated questions JSON can be saved in a GIT repo and exposed as an API using Python Flask/Fast API on cloud foundry buildpack. Frontend Application here is built on React. Here is a reference Quiz Open source App which can be customized to read from a Private API instead of Open Trivia Database : GIT

BTP Solution Components

AI core & Gen AI Hub : App logic in Python in a docker image(GPT call / prompt management)

AI Launchpad : ML Ops , operations and monitoring of deployed API.

React App: FrontendDeployed on Cloud foundry Node buildpack.

Conclusion

SAP BTP has a powerful platform to quickly build highly scalable applications and further infuse Gen AI using the technologies & products that we explored in this article. I'm happy to collaborate and provide more inputs on any of the applications I have presented in the use cases section.<Please note that these were self initiated projects for personal learning and development >.

Generative AI-powered Contract document processing with Large Language Models

2024-02-19T03:54:18.101000+01:00

Introduction

Contract Document Processing Use Case

Automate the extraction of relevant information from contract documents with Document Information Extraction with Generative AI (premium edition) by leveraging the capabilities of Large Language Models, ensuring faster, more accurate document processing with significantly reduced processing costs.

Process Supply Contract using Generative AI

Document Information Extraction Service does not come pre-configured with support for custom document type - Contract. So, let us create a custom schema utilising Large Language Models (LLMs) to specify the fields we wish to extract from the supply contract document.

Schema Creation for Supply Contract

Access Document Information Extraction Application as described in the tutorial Use Trial to Set Up Account for Document Information Extraction and Go to Application and create new schema.

Create Supply Contract Schema

Supply Contract Schema

Addition of Fields

Access the supply contract schema and add fields at header and line item level.

Add Data Field

Note:

LLMs are used to extract the data from the document when you use setup type auto.

Added Fields

Tip:

When adding field names and descriptions, consider yourself explaining what you want to extract to someone who doesn't know anything about it already.

The better you describe the information that you want to extract using generative AI, the better your results will be.

Similarly, fields can be added at line item level.

Schema Activation

Now, activate the supply contract schema.

Activated Schema

Obtaining Extraction Results

Upload the contract document (sample below) using the newly created supply contract schema.

Sample Supply Contract

Supply Contract Schema associated Document Upload

Extraction Results

Conclusion

With the Schema Configuration feature from Document Information Extraction and LLMs, we have now successfully extracted information from a supply contract document without the need to create any templates.

Similarly, Document Information Extraction with Generative AI can be utilised for extracting structured information from other types of contracts.

Further Information

Please feel free to add a comment, should you have any queries.

Best Regards,
Anuradha Dhingan

SAP Fiori Innovation Day in Dublin - demos, implementation tips, and lots of AI examples

2024-02-26T10:54:16.516000+01:00

We hosted our first SAP Fiori Innovation Day in our Dublin, Ireland office on Feb 21, 2024. As the name implies, it was a mix of helping customers understand how to implement SAP Fiori in their organizations and a look at innovations that will make people more productive by using artificial intelligence. We had a variety of lectures, examples, hands-on workshops, practical tips for implementing SAP Fiori, and demos of a variety of AI use cases throughout the day.

After an introduction by Liam Ryan, the managing director of SAP Ireland, the chief product owners of SAP Cloud ERP UX Engineering, Katja Zoch and Sebastian Steinhauer, kicked off an overview of our strategic direction and a discussion on “No-frills AI in SAP S/4HANA.” The no-frills refers to simple interfaces that make life easier for both end-users interacting with SAP Fiori elements apps and developers creating those apps. Functionality over fanciness. The presentation incorporated some new AI innovations being delivered as part of SAP S/4HANA.

Chief product owners of SAP Cloud ERP UX Engineering,
Katja Zoch and Sebastian Steinhauer presenting at the SAP Fiori Innovation Day in Dublin

We continued the day with demos of My Home and Collaborative ERP, two recent innovations that make SAP S/4HANA Cloud more intuitive than ever before by combining a mixture of human and machine intelligence. After showing the latest and greatest in our user experience portfolio, we pivoted to options that customers have today for rolling out SAP Fiori apps in their environment. We touched on the SAP Fiori app reference library, how to find the apps you need, and activate them in your environment.

SAP partner mymediset discussed their journey from SAP GUI to SAP Fiori elements using our latest tooling, and how using SAP Fiori tools gave them a boost in efficiency. Adaptation projects make it easy for customers to extend their systems while keeping a clean core.

After lunch, another SAP partner, sovanta, discussed the importance of designers and developers working together to ultimately serve the needs of the business user. Good communication based on the SAP Fiori design guidelines helps to keep the project focused.

The bulk of the afternoon was breakout sessions. In our hands-on track, participants had the opportunity to build an SAP Fiori elements app using the flexible programming model. This was a chance to use the new SAP Build Code, which contains SAP Business Application Studio, SAP Fiori tools, and many other application development accelerators.

Non-developers had the opportunity to dig into the business benefits of using SAP Fiori tools to build SAP Fiori elements applications, a closer look at how the design process should run to streamline development, along with two more demos of using AI to solve focused business problems. One demo was on automating user assistance to improve the speed of creating documentation along with its quality. The other focused on how AI can help business resolve disputes more easily.

The final sessions were also on AI. First up, SAP partner Bluestonex discussed how to embrace AI in the enterprise and talked about the AI capabilities they have implemented using SAP Fiori.

The day finished with a talk from the SAP Fiori tools team. The presentation focused on the increasing prevalence of AI in the enterprise space, and how SAP is bringing AI innovations to customers. It included demos using generative AI to create SAP Fiori elements applications directly from a customer business requirement document and finished with a demo converting a sketch on a flipboard into an SAP Fiori elements application without writing any code.

Agenda:

The event would not have been possible without support from our generous sponsors. Please visit their websites to see how they can help you accelerate your SAP Fiori journey and add intelligence to the applications you rely on to run your business.

If you missed it, we plan to have more SAP Fiori Innovation Days during the year.

Learn about some of the topics we discussed:

Subscribe to the SAP Fiori development newsletter, in which we share updates on SAP Fiori elements, SAP Fiori tools, SAPUI5, and SAP Business Application Studio every other month.

Online information about SAP Fiori development tools, technologies, and adjacent topics.

SAP Community topic page for SAP Fiori elements: www.sapfiorielements.com
SAP Community topic page for SAP Fiori tools: www.sapfioritools.com
Flexible Programming Model explorer: https://sapui5.hana.ondemand.com/test-resources/sap/fe/core/fpmExplorer/index.html#/overview/introduction
SAP Joule: https://www.sap.com/products/artificial-intelligence/ai-assistant.html
SAP Fiori design guidelines: https://experience.sap.com/fiori-design/
SAP Fiori app reference library: https://fioriappslibrary.hana.ondemand.com/sap/fix/externalViewer/

Courses with self-paced learning on SAP Fiori app development and SAP Fiori usage

openSAP course on Developing and Extending SAP Fiori Elements Apps: https://open.sap.com/courses/fiori-ea1
openSAP course on SAP S/4HANA UX for Business and Functional Experts: https://open.sap.com/courses/s4h34

HyperAutomate Inter-company Tax Calculations using SAP Build Process Automation

2024-02-27T22:19:25.266000+01:00

Business Challenge

Companies have a legal and regulatory obligation to charge taxes on inter-company transactions, regardless of whether the companies involved are under their control or not. However, the current process of handling these transactions can be highly risky and time-consuming. It often requires a significant amount of effort for correction, running, re-running, and rebuilding. Moreover, the existing solution is not scalable enough to handle the growing demand and increasing volume of data. With business relationships becoming more complicated, there is now a greater need for a flexible, scalable, and efficient solution that can meet these requirements. This solution should be able to handle a large volume of data while ensuring accuracy and compliance with legal and regulatory obligations.

Solution Scope

To enhance the inter-company tax calculation process.
Allow business users to have complete control over input parameters (from any device).
Automate the posting of journal entries for each company code for sales/purchase tax.
Create PDF invoices dynamically that reflect inter-company charges and attach them to the corresponding journal entry.
Optimize performance to prevent any issues faced in the existing automation.

Solution Architecture

The following image illustrates the ITC architecture diagram of the customer’s productive solution landscape: 

Process Flow

An innovative Fiori UI application has been developed to provide business users with comprehensive control over input parameters and automation triggers, enabling them to manage their financial transactions with ease. This application can be conveniently accessed from any device, ensuring that users can perform their tasks without interruption.

Upon initiation of the trigger, the automation program automatically calculates tax amounts from customer line items and subsequently posts journal entries for each company code in the background, minimizing the need for manual intervention. The application is also equipped with the ability to dynamically generate PDF invoices that reflect inter-company charges for each journal entry.

These invoices are attached to their corresponding journal entries, providing users with an efficient means of tracking, and managing their financial transactions. The execution logs are securely stored and can be retrieved for validation and auditing purposes, ensuring that all transactions are recorded accurately and can be audited at any time. The following image illustrates the inter-company flow diagram of the solution design:

Fiori App for ICT Consolidated Inputs

The following are the two Fiori UX applications that were created to facilitate user-driven automation:

Fiori App for Tax Rate Maintenance:

This use-case was able to demonstrate considerable business outcome both on business and financial KPIs as below:

Key Highlights:

Customer partnered with the SAP MaxAttention team who delivered the prototype in less than 4 weeks and supported the production deployment in less than 8 weeks.
Transaction Volume – Savings of $1.176 Million Interest and Fines Exposure (annually)
40 pairings with over 5K customer line items per execution
To lower TCO and improve ROI, the solution is developed using SAP Business AI, SAP Build Process Automation, S/4HANA REST APIs, Fiori UI, and SAP HANA Cloud
The key highlight of the solution was leveraging the Customer’s existing investments in SAP, improving, and optimizing the solution for evolving and challenging business needs with quick time to value.
The Intercompany Tax Automation project helped customer clear the backlog of vendor invoices and improve their reputation with its vendors.
Vendors are satisfied with the timely processing of payments.
Business users are happy with the solution, and the adoption is very high due to the reduction in tedious manual intervention and data processing; end users are now able to focus on strategic tasks rather than mundane data validation activities.
Customer accelerated the month-end closing process and avoided paying penalties and fines due to tax compliance issues.

Following are the additional SAP MaxAttention customer success stories using SAP Build Process Automation:

SAP Build Product Management: Steven Jacobson, Andre Borchert
SAP MaxAttention CoE: Kiran Kola, Rohit Dwivedi

Azure AI Studio - Explore Speech AI feature.

2024-02-29T17:23:06.424000+01:00

Introduction:

Hello SAP members!!

I am back again with another interesting topic that captures your attention for a moment. Microsoft Azure AI Studio with a powerful Speech AI feature, enabling developers to integrate speech recognition, synthesis, and understanding into their applications seamlessly. With Azure Speech AI, developers can create interactive voice response systems, transcribe audio recordings, and even build sophisticated virtual assistants for Automations.

Leveraging state-of-the-art machine learning algorithms, Azure Speech AI empowers developers to unlock new possibilities in communication and interaction. From enhancing accessibility to revolutionizing customer service, Speech AI in Azure AI Studio opens doors to innovative solutions in the realm of human-computer interaction. Interesting thing is you can select different voice modulations that are pre-build for AI Speech interactions.

I have a small videoclip that walks you through the platform, where you'll experience the AI voice (that I have used- "Ava") utilized for custom app interaction.

Let's start without further delay.

Azure AI Studio:

Azure AI Studio serves as a consolidated web portal that integrates various Azure AI services into a cohesive development environment. It encompasses:

The capabilities of Azure Machine Learning service, including the model catalog and prompt flow development.
The deployment, testing, and custom data integration features for generative AI models from Azure OpenAI service.
Seamless integration with Azure AI Services, covering speech, vision, language, document intelligence, and content safety.

Note: In some cases, Azure AI services are integrated directly into to the Azure AI Studio interface and the underlying Azure AI Service resource. In other cases, a link is provided to external service-specific studios where you can create and use Azure AI services resources. In either case, Azure AI Studio provides a central starting point from which you can find and integrate Azure AI services into your solution.

Azure AI Studio Features:

Azure AI Studio enables teams to collaborate efficiently and effectively on AI projects, such as developing custom copilot applications that use large language models (LLMs). Tasks you can accomplish with Azure AI Studio include:

Deploying models from the model catalog to real-time inferencing endpoints for client applications to consume.
Deploying and testing generative AI models in an Azure OpenAI service.
Integrating data from custom data sources to support a retrieval augmented generation (RAG) approach to prompt engineering for generative AI models.
Using prompt flow to define workflows that integrate models, prompts, and custom processing.
Integrating content safety filters into a generative AI solution to mitigate potential harms.
Extending a generative AI solution with multiple AI capabilities using Azure AI services.

Azure AI resource:

An Azure AI resource is the foundation for AI development projects on Azure, and enables you to define shared assets that can be used across multiple projects. You can use AI Studio to perform the following tasks in an Azure AI resource on the Manage page:

Create members and assign them to specific roles.
Create and manage compute instances on which to run experiments, prompt flows, and custom code.
Create and manage connections to resources, such as data stores, GitHub, Azure AI Search indexes, and others.
Define policies to manage behavior, such as automatic compute shutdown.

Azure AI projects:

An Azure AI resource can host one or more projects. Each project encapsulates the tools and assets used to create a specific AI solution. For example, you might create a project to enable data scientists and developers to collaborate on building a custom copilot for a business application or process.

Once you created a resource and project you can able to see below interface where you can train,develop and deploy your model.

I have enabled speech feature in playground so you can interact with model with voice commands.

Here is small video of my application,

After deploying the application, we can see Launch Button next to Deploy option. Thus, your deployment will complete and clicking on Launch button will navigate to our application.

Application is live and ready to use. Best feature is you can share this app to your fellow team members. Isn't that awesome! 😊

Conclusion:

This way we can use Azure AI services to create our own app using custom model. You can also choose existing models based on your requirement and feed data to train your model. I believe speech AI for copilot apps will be more interesting. Thanks for your time to read this blog.

As always Happy Learning...Spread the knowledge...

Reference Links:

https://learn.microsoft.com/en-us/azure/ai-studio/

https://learn.microsoft.com/en-us/azure/ai-studio/what-is-ai-studio?tabs=home

Unlock the Future of SAP: Don't Miss These Sessions at SAP Sapphire 2024!

2024-03-05T12:00:00.028000+01:00

Get ready for SAP Sapphire 2024! We're thrilled to unveil an incredible lineup of sessions that promise to revolutionize the way you leverage SAP technology. Join us in Orlando, USA from June 3 to June 5, 2024, for an unforgettable experience packed with insights, innovations, and opportunities for growth. Here's a sneak peek at some of the sessions you won't want to miss:

SER207 Translating Generative AI and LLMs into Business Benefits
Explore how generative AI and large language models (LLMs) are reshaping customer support offerings, enhancing internal efficiencies, and accelerating the customer experience. Learn about the design, implementation, and integration of LLMs to elevate your support capabilities to new heights.

SER205 “SAP for Me”: Bundles Unleashed
Discover the power of bundles in the “SAP for Me” portal, revolutionizing the way businesses manage cloud services on SAP Business Technology Platform. Learn how intuitive management and customer-centric language empower businesses to grasp the full value of their cloud services, driving efficiency and productivity.

SER202 Reinventing Support with AI
Dive into how intelligent automation and AI are transforming support offerings to deliver unparalleled customer experiences. Gain insights into predictive, bi-directional support and explore our journey towards delivering outstanding value to customers.

SER201 Unlocking Growth: Air Liquide's Success with SAP Cloud ALM
Learn how Air Liquide Advanced Materials leverages SAP Cloud ALM to support end-to-end business processes, driving improvement opportunities, change management, and user adoption through quantifiable process performance.

SER204: SAP Cloud ALM for operations at Brightspeed
Discover the value-driven benefits Internet provider Brightspeed experienced using SAP Cloud ALM for operations. Learn how the company uses the offering in areas from business process monitoring, integration, and exception monitoring to job, automation, and health monitoring.

SER206 Migrating to SAP S/4HANA: Confident Data Transition
Unlock value in transitioning to SAP S/4HANA with the SAP Business Transformation Center solution. Explore selective data-transition intricacies and accelerate your journey with smooth data transformation.

SER203 Release the Value of AI in Your Business Transformation
Witness the transformative power of AI integration into business processes, demonstrated by our customer support team. Explore the tangible value AI brings to your digital transformation journey through SAP Signavio solutions.

SER200 Empowering Your Cloud Journey: Unleashing Transformation with SAP Cloud ALM
Create momentum in your move to the cloud with SAP Cloud ALM. Discover how our partner ecosystem supports your transition and explore the transformation-suite approach to boosting your change process.

SER227 Optimizing Innovation: Roadmap for SAP Cloud ALM
Gain insights into adopting clean core principles, managing extensions, and enabling efficient operations with SAP Cloud ALM. Explore integration with SAP Business AI solutions and make a plan to transition from SAP Solution Manager.

These sessions promise to deliver invaluable insights and practical strategies to drive innovation, efficiency, and success in your SAP journey. Don't miss out on this opportunity to unlock the full potential of SAP technology and propel your business forward. And remember, our executives and experts will be available within the SAP Services and Support Center onsite at the event. Don't miss the chance to engage with them directly. Get registered now. Book your appointment in advance! See you at SAP Sapphire 2024!

10+ ways to reshape your SAP landscape with SAP Business Technology Platform – Blog 1

2024-03-14T15:09:45.713000+01:00

Today, companies are exposed to unprecedented combinations of threats and uncertainties by economics, new competitors, wars, extreme weather events, etc. Therefore, businesses must react to this. One the one side, they need to increase agility to become as flexible, fast, and resilient as possible. This involves a new speed of innovation and the capability to tailor customer experience to individual needs. On the other side, they need fully digitized processes within and beyond the company, across the complete value chain.

In such a demanding environment business needs a powerful IT that can implement new business requirements fast and leverage new technologies such as AI.

Many SAP systems such as SAP ECC have been up and running for decades. Technologies available at that time looked totally different. Today most processes need to be automated to minimize the number of manual steps and maximize the transaction speed. By embracing the cloud, processes can be implemented much faster. Accordingly, one important step in transformation is to determine which components remain on-prem and which processes and systems should be moved to the cloud. Especially extensions and apps are often developed faster in the cloud due to much smaller lead times.

Modernization is not limited to a single ERP and should include data management, development, integration, and steering across the full enterprise architecture, covering SAP and non-SAP systems.

This is exactly where the SAP Business Technology Platform comes into play as a booster of the Digital Transformation of your systems across 4 dimensions:

Extend your applications.
Automate & integrate business processes.
Unleash your data with Data & Analytics.
Modernize your Steering.

On top of the four domains, Clean Core and AI become key in 2024.

Clean Core:

The concept of Clean Core customers can adopt to make their IT future-proof. The Core of the system, the ERP has very often a long history of modifications. Upgrades take a long time and can be very costly. New requirements often took one to two years until they were taken up into the next release schedule. With the faster changing business environments and the need to digitize much faster, the ERP systems need to be cleaned up to become more flexible and agile.

This is where the Clean Core Concept can help. The goal is to eliminate modifications with three toolsets available:

The Key-User extensibility in S/4 to enhance fields and small logic.
The On-Stack extensibility based on the ABAP Cloud. This is a complete toolbox to adapt ABAP coding. The name ABAP Cloud is a bit misleading because it can available directly embedded on your S4 system on-prem and doesn’t require any cloud services on SAP BTP.
Side-by-side extensibility on SAP BTP. Bigger developments, mobile apps and automations would typically be done as loosely coupled side-by-side extensions on the BTP.

In the concept, customers determine which type of developments they do on which tool. With eliminated modifications and a “clean system” new requirements and new apps can be implemented much faster and upgrades go faster and with reduced costs. Clean Core is not a one-time task but an ongoing exercise making your IT systems future-proof.

AI:

AI is experiencing a huge hype. The challenge is to implement the technology into real business processes. AI today at many customers is technically capable but somewhere disconnected. It is trained on some data but it doesn‘t know much about the data, the processes, the people that can use it etc.

This is where SAP Business AI comes into play with its business centricity in three dimensions:

The business context with access to semantic data, which is always available and using AI as an extension of the business process and the context being aware who can perform at which point which task and why they need to do it.
The business-friendly experience with integrated seamless access and embedded into the normal SAP business user.
The accelerated business outcomes with prebuilt content, actionable use cases that can be directly rolled out, automation with built-in AI, and smart support based on the context and business know-how.

The offering is relevant, reliable, and responsible. It is relevant - because AI will be embedded into all SAP-cloud-based applications. It is reliable because it is based on the customer’s unique business data and business context. And finally, it is responsible - because we will deliver AI with the highest levels of concern for security, privacy, compliance, and ethics.

The Blog Series 10 + ways to reshape your SAP Landscape with SAP BTP will be published regularly and the newest blogs can be found here: Video & Blog Series - SAP BTP MEE Overview (brandcast.io)

To get more insights, please also visit one of our BTP Innovation Days:

Innovation Day Vienna: Vienna 04-Apr-24 SAP BTP Innovation Day Vienna
Innovation Day Switzerland: Bern 14-May-24 SAP | SAP BTP Innovation Day Switzerland
Innovation Day Germany Essen 16-May-24 SAP | SAP BTP Innovation Day

10+ ways to reshape your SAP landscape with SAP Business Technology Platform – Blog Series

2024-03-18T17:56:24.598000+01:00

Blog 1: The Central Role of Clean Core and AI

Summary: In this blog series we will look at the role of the SAP Business Technology Platform to reshape your SAP landscape including SAP and Non-SAP systems. In this first blog we discuss why Clean Core and AI are so important on your path to a future-proof IT.

Today, companies are exposed to unprecedented combinations of threats and uncertainties by economics, new competitors, wars, extreme weather events, etc. Therefore, businesses must react to this. One the one side, they need to increase agility to become as flexible, fast, and resilient as possible. This involves a new speed of innovation and the capability to tailor customer experience to individual needs. On the other side, they need fully digitized processes within and beyond the company, across the complete value chain.

In such a demanding environment business needs a powerful IT that can implement new business requirements fast and leverage new technologies such as AI.

Modernization is not limited to a single ERP and should include data management, development, integration, and steering across the full enterprise architecture, covering SAP and non-SAP systems.

This is exactly where the SAP Business Technology Platform comes into play as a booster of the Digital Transformation of your systems across 4 dimensions:

Extend your applications.
Automate & integrate business processes.
Unleash your data with Data & Analytics.
Modernize your Steering.

On top of the four domains, Clean Core and AI become key in 2024.

Clean Core:

This is where the Clean Core Concept can help. The goal is to eliminate modifications with three toolsets available:

The Key-User extensibility in S/4 to enhance fields and small logic.
The On-Stack extensibility based on the ABAP Cloud. This is a complete toolbox to adapt ABAP coding. The name ABAP Cloud is a bit misleading because it can available directly embedded on your S4 system on-prem and doesn’t require any cloud services on SAP BTP.
Side-by-side extensibility on SAP BTP. Bigger developments, mobile apps and automations would typically be done as loosely coupled side-by-side extensions on the BTP.

AI:

The business context with access to semantic data, which is always available and using AI as an extension of the business process and the context being aware who can perform at which point which task and why they need to do it.
The business-friendly experience with integrated seamless access and embedded into the normal SAP business user.
The accelerated business outcomes with prebuilt content, actionable use cases that can be directly rolled out, automation with built-in AI, and smart support based on the context and business know-how.

The Blog Series 10 + ways to reshape your SAP Landscape with SAP BTP will be published regularly and the newest blogs can be found here: Video & Blog Series - SAP BTP MEE Overview (brandcast.io)

To get more insights, please also visit one of our BTP Innovation Days:

Innovation Day Vienna: Vienna 04-Apr-24 SAP BTP Innovation Day Vienna
Innovation Day Switzerland: Bern 14-May-24 SAP | SAP BTP Innovation Day Switzerland
Innovation Day Germany Essen 16-May-24 SAP | SAP BTP Innovation Day

Augmenting SAP BTP Use Cases with AI Foundation: A Deep Dive into the Generative AI Hub

2024-03-25T15:08:29.967000+01:00

Authors: Alice Magnani, Cesare Calabria, Jacob Tan, Mostafa Sharaf, Trinidad Martinez.

Generative AI is accelerating in the industry, with a McKinsey report stating that Generative AI could add between $2.6 trillion to $4.4 trillion in incremental value to the global economy annually. Companies globally are increasingly using Generative AI, with about one-third of them using it regularly in at least one business function.

At SAP, we integrate Generative AI technology with industry-specific data and process knowledge to create innovative AI capabilities for our applications. To illustrate how Generative AI can benefit businesses, in this blog we'll walk you through a citizen reporting app for the public administrations industry.

Our use case involves a fictitious city, "Sagenai City", struggling with managing and tracking maintenance in public areas. The city wants to improve how it handles reported issues from citizens by analyzing social media posts and making informed decisions. A better perception of public administration by citizens is an anticipated outcome.

The goal of the citizen reporting app is to assist the Maintenance Manager by extracting insights from citizen social posts, classifying them, and creating maintenance notifications in the SAP S/4 HANA Cloud tenant.

Let’s understand the Citizen Reporting App business flow:

A citizen reports an incident through a post on the city's community page on Reddit (we have used Reddit as having a rich and free API, but any social media of your choice can be chosen).
The citizen reporting app receives the post and notifies the responsible persons from the public administration office.
The post is then processed and analyzed by a Large Language Model through SAP’s Generative AI Hub to extract key points and derive insights such as summarizing the issue, identifying the issue type, its urgency level, determining the incident's location, and analyzing the sentiment behind the post.
The Maintenance Manager reviews the incident details extracted by Generative AI and decides whether to approve or reject the incident. As a result, the manager saves time through the reporting process, thanks to the powerful text analysis by Generative AI.

To demonstrate this in action please check the following demo video of the Citizen Reporting App, leveraging the power of SAP’s Generative AI.

Before we go into details on how we implemented this proof of concept, let’s review some key concepts.

LLM’s Overview

Large Language Models (LLMs) have been making waves in the artificial intelligence (AI) landscape. These foundation models trained on broad datasets have expanded their influence beyond natural language to domains such as videos, speech, tabular data, and protein sequences.

Understanding Large Language Models

Foundation models, a term coined by Stanford researchers, are self-supervised models trained on vast datasets. They can be easily adapted to a myriad of downstream tasks. Among these, Large Language Models (LLMs) focus on natural language in the form of text.

In some cases, LLMs also exhibit impressive generative capabilities, making them suitable for creating novel content. They are often referred to as generative AI models.

In this blog we will focus mainly on the LLMs application around Generative AI.

These models leverage transfer learning, a concept that has been in use for decades in the image domain, on a much larger scale. The change of scale was made possible by a set of key-factors, such as improved computing capabilities, the advent of the transformer architecture, and the availability of massive datasets. As a consequence, these large models started to show new emerging capabilities. One of them is in-context learning, which allows to adapt the model to perform a certain task with no need for additional training, just giving instructions in human language in the form of text. This can reduce tremendously the time to value required to implement artificial intelligence for a wide set of tasks.

Also, LLMs are typically trained on the task of predicting the next word in a text. This approach requires the model to gain substantial knowledge about the world, making them a form of "lossy compression" of the information available on the internet.

Limitations of Large Language Models

Despite their remarkable capabilities, LLMs come with some limitations. First of all, training these models is time-consuming and expensive. Furthermore, keeping the models' knowledge correct and updated is a challenge. They are also stochastic, meaning minor variations in the prompt can lead to different outputs. There is no way to verify the correctness of the models' answers and finally, they tend to prioritize form over content, so they can generate incorrect but plausible sounding answers.

With the current state of the art, LLMs can help solving many uses cases requiring language understanding and language generation. LLM struggle, instead, in logics and math, and with use cases that require analysis of numerical, structured data. In the following image we share some use case samples that we have classified into good LLM use case and not such a good case for LLMs.

Adapting LLMs to Desired Tasks

There are several strategies to adapt LLMs to the desired task and work-around some of their limitations. The most widely used are listed below:

Prompt engineering.
Prompt engineering is the process of designing and refining prompts or instructions given to a language model to elicit desired responses.
The quality of the prompt can greatly help in improving the response model's accuracy, controlling its biases, or generating creative and coherent responses.
Retrieval Augmented Generation (RAG).
RAG idea is to provide the model with a knowledge base (a document or a corpus of documents) and ask it to answer our prompts based on these documents.
Fine-tuning.
Fine-tuning refers to the process of taking a pre-trained model and adapting it to perform a specific task or on a specific dataset. In machine learning, models are typically trained on large datasets to learn general patterns and features. Fine-tuning allows to take advantage of this pre-trained knowledge and apply it to a more specific task or dataset.
The costs involved in fine-tuning can be considerable as they might include computational resources, such as GPU or cloud computing costs, as well as the time and effort required to collect and preprocess data, train the model, and evaluate its performance.

Choosing the Right LLM

When choosing an LLM, it's important to consider factors like price, latency, and request limits. Latest and larger models are usually more capable and robust. Consider looking at model benchmarks and leaderboards, such as Chatbot Arena, to compare the performance of different models.

Evaluating LLMs

Before putting an LLM-based application into production, it is always best practice to test the performance of the LLM for your specific task. To do that, you can come up with a sample of data points to test your model. Apply your LLM prompt in batch to the sample and then evaluate the LLM response accuracy. Sometimes, this operation can be challenging, maybe because of the lack of data points to use for testing, or because it is difficult to rate how good is the model output when a language-related task is involved. LLMs can be useful to overcome these challenges. For instance, you can ask an LLM to generate test data points for you. Also, you can use an LLM as a judge, to rate the quality of an LLM-generated text with the criteria that are important for you, for instance creativity, correctness, etc.

Libraries / LangChain
There are libraries bridging the gap between traditional software development and Generative AI, to make it easier to implement generative AI technology in an application. Langchain is one of the most well-known but there are other options.

Langchain is available for python and Javascript, and it offers many templates to build prompt effectively, parsers to parse the response output, chains to build sequences of LLM operation, Agents, and model evaluation. Langchain can be used in combination with SAP tools for Generative AI, we will talk about it later in this blog.

Overview on SAP’s Generative AI Hub

Artificial Intelligence (AI) is revolutionizing every aspect of business, and at SAP, we are committed to harnessing this power to drive business transformation. Our focus is on Business AI, which means AI for every aspect of the business. We continuously strive to build the best technology and leverage the best tools on the market, including strategic partnerships with industry leaders.

SAP Business AI Strategy

Our SAP Business AI strategy is embedded across various business domains including Finance, Supply Chain, HR, Procurement, Marketing and Commerce, Sales and Services, and IT. We offer a complete set of services that enable developers to create solutions with artificial intelligence. These services form the AI Foundation from which developers can pick whatever they need to build their AI- and generative AI-powered extensions and applications on SAP Business Technology Platform (SAP BTP).

SAP BTP AI Portfolio

Our AI portfolio includes SAP AI Services to help you automate and optimize corporate processes by adding intelligence to your applications using AI models pretrained on business-relevant data. These services include document information extraction, document classification, personalized recommendations, data attribute recommendation, and translation of software texts and documents.

As we have seen, Generative AI, a form of artificial intelligence, can produce text, images, and varied content based on its training data. At SAP, we integrate Generative AI with extensive industry-specific data and deep process knowledge to create innovative AI capabilities for the SAP applications you use every day. It’s built-in, relevant to your business, and responsible by design.

We also offer the Generative AI Hub, which provides instant access to a broad range of large language models (LLMs) from different providers. The hub provides tooling for prompt engineering, experimentation, and other capabilities to accelerate the development of your SAP BTP applications infused with generative AI, in a secure and trusted way.

Generative AI Hub

The Generative AI Hub allows access to a large variety of foundation models, from the hosted or open-source ones to the proprietary and remote models and in the future also models built or fine-tuned by SAP.

The Generative AI Hub is a new SAP BTP capability offered across two different services: AI Core and AI Launchpad.

Deployment of LLMs in SAP AI Core

To consume the LLMs through SAP’s Generative AI Hub, we first need to deploy them in SAP AI Core (Create a Deployment for a Generative AI Model). We leverage AI Core capabilities to provide a standard model inference platform on Kubernetes and serve the LLMs taking care of security aspects, multi-tenancy support, ...

Check the Models and Scenarios in the Generative AI Hub up-to-date list of supported models.

Generative AI Hub Capabilities in SAP AI Launchpad

SAP AI Launchpad provides a graphical interface for Generative AI Hub and from there several tools are provided for the developers. These include a playground system where developers can perform prompt engineering and test prompts against different LLMs, and Prompt Management features that allow storing and versioning prompts.

Generative AI Hub SDK

The Generative AI Hub SDK is a collection of tools, in addition to the existing ai-core-sdk, to support LLM access. It boosts efficiency when working with various LLM models by streamlining the deployment of LLM models and the querying of available models.

At the time of writing this blog, only the Python version is made GA (Generable Available), stay tuned for a JavaScript version!

Deep Dive into the Citizen Reporting App Use Case

In the previous sections, we've explored the concept of Generative AI and how SAP's Generative AI Hub can help you implement your own solutions. Now, we're going to delve deeper into our specific use case: the Citizen Reporting app.

Business Scenario

The Citizen Reporting app is designed to streamline the process of incidents reporting in social media. The app leverages the capabilities of the Generative AI Hub to gain insights from citizens' social posts. This not only saves time for maintenance managers but also improves the public's perception of the administration.

The app allows maintenance managers to easily review and manage incidents, including AI insights and classification. Once the relevant incidents are supervised, the app creates Maintenance Notifications in the SAP S/4HANA Cloud tenant.

Solution Architecture

The Citizen Reporting app consists of a server-side application that can be run in SAP BTP Cloud Foundry or Kyma runtime, and a user interface developed with SAP Build Apps. The server-side application provides all the APIs required to get data and start the execution of the actions required by the maintenance manager.

The server-side application consumes APIs from different BTP Services, including the SAP AI Core service, which provides Generative AI APIs from different foundation models in a trusted and controlled way.

The Destination service is used to securely connect to the SAP AI Core APIs as well as SAP S/4HANA Cloud APIs to automatically create Maintenance Requests after Maintenance Manager’s approval.

Implementation Steps

Let’s go a bit deeper into the implementation by having a look to the different implementation steps:

SAP BTP Setup

To work with Generative AI hub in SAP Business Technology Platform, the first requirement is to have an SAP BTP sub account with an instance of SAP AI Core. The SAP AI Core instance will provide us with a set of APIs to consume Generative AI Hub from our implementation.

Once we have our instance of SAP AI Core, we can create instance of the SAP AI Launchpad to have access to a user interface to configure and manage our generative AI deployments.

As we saw in the architecture diagram, other SAP BTP services like the Cloud Foundry environment, the SAP HANA Cloud Service, SAP Authorization and Trust Management Service (XSUAA) & Destinations Service, would be required for the application and the service instances will need to be created.

Prompt Engineering

Once your SAP BTP account is ready, we will build the prompt to be sent to the Foundation Model. This is a very important phase to get accurate insights from SAP’s Generative AI Hub. The prompt engineering step is usually recursive and conducted with the help of tools like the AI Launchpad or a Jupiter Notebook, to test and fine-tune the prompt.

Let’s review how we built our prompt to get insights from the Citizen’s social media posts.

Application Skeleton

The application flow begins with citizens reporting incidents on social media. The server-side application reads these posts and sends their content, along with a prompt, to SAP's Generative AI Hub. The SAP’s Generative AI Hub generates insights from each post, which are then presented to the Maintenance Manager.

The manager can choose to approve or reject each incident. Approved incidents result in a maintenance notification being created in SAP S/4 HANA, while all incidents, approved or rejected, are logged in the SAP HANA Cloud database.

We have implemented this application by building three different modules:

Social Media integration
Generative AI Hub integration
SAP S/4 HANA Cloud integration.

The full source code of our application is available in the SAP-samples/btp-generative-ai-hub-use-cases GitHub repository part of the SAP-Samples.

Consuming Generative AI Hub in JavaScript

To implement the server side of our social media citizen reporting app in JavaScript, we have leveraged the SAP Cloud application programming model.

The JavaScript implementation shown in this demo directly calls the SAP AI Core Generative AI Hub APIs through an HTTP POST and shows how to consume the SAP’s Generative AI Hub raw APIs. Please check Generative AI Reference Architecture including a GitHub repository to check other implementation options in TypeScript leveraging Langchain.

Consuming Generative AI Hub in Python

To consume Generative AI Hub in Python, we leverage the Generative AI Hub SDK, which simplifies the implementation of the calls to the SAP’s Generative AI Hub APIs.

SAP S/4 HANA Cloud Integration

Once the Maintenance Manager approves an incident, a maintenance notification is created in the SAP S/4 HANA Cloud tenant. We used the SAP Cloud SDK Maintenance Notification Service module to implement this functionality. The user interface calls a function to create the maintenance notification, providing all the required information from the citizen post and the insights generated by the Generative AI Hub.

User Interface Implementation

The user interface was implemented using SAP Build Apps, a low-code, no-code tool that enabled us to implement our proof of concept quickly, using ready-to-consume UI components. The user interface connects to the backend via OData APIs to retrieve all the details required by the Maintenance Manager.

All the source code of this application is available on our Github repository for further exploration and includes both the JavaScript as well as the Python version of the backend application among other resources.

Explore further resources on generative AI at SAP

Source code of our prototype

SAP-samples/btp-generative-ai-hub-use-cases

Generative AI at SAP

Implementation Samples

Social Media Citizen Reporting App – GitHub repository
Generative AI Reference Architecture
Generative AI Hub Tutorials

More sessions on this series

Please check the central blog Augment Your SAP BTP Use Cases With AI Foundation on SAP BTP to get all the details on the sessions part of this series, register for the upcoming sessions and earn a knowledge badge by attending passing the associated web assessment.

Conclusion and Key Takeaways

Large Language Models have ushered in a new era in the field of artificial intelligence. While they come with their limitations, their ability to generate novel content and adapt to a wide range of tasks makes them a powerful tool. By understanding these models and how to adapt, choose, and evaluate them, we can leverage their capabilities in various SAP environments and beyond.

The SAP’s Generative AI Hub offers a streamlined toolset for Generative AI solution development, instant access to top-rated foundation models from multiple providers, and an elevated level of trust and control.

We hope this blog has inspired you to start designing and implementing your own use cases to unlock the power of generative AI in your solutions. Stay tuned for more blogs in this series as we continue to explore the exciting possibilities of generative AI with SAP!

Harnessing Generative AI Capabilities with SAP HANA Cloud Vector Engine - Part 1 [Architecture]

2024-03-27T07:27:12.706000+01:00

As we approach the close of the first quarter, we're excited to announce the availability of the "Vector Engine" feature in SAP HANA Cloud as part of the QRC1 Release. This new feature is set to enhance the multi-model capabilities of SAP HANA Cloud by incorporating vector database capabilities for storing embeddings. Embeddings are instrumental in transforming high-dimensional data into a more manageable, lower-dimensional format, thereby simplifying the understanding of complex and unstructured data such as text, images, or user behavior.

Moreover, the integration of the SAP HANA Cloud Vector Engine with the Generative AI Hub for accessing Large Language Models (LLMs) will empower our customers to develop robust AI-based applications and reporting solutions, all while complying with our governance and ethical framework.

Numerous blogs on our SAP Community/Medium delve into the concepts of the SAP HANA Cloud Vector Engine. For a comprehensive understanding of the basics and features of SAP HANA Cloud, Shabana's blog is a great resource. In this discussion, we will concentrate on the use case we published for the Discovery Mission and the scenarios it encompasses.

We have launched an SAP Discovery Mission that covers the basics of SAP HANA Cloud Vector Engine, embedding texts by accessing the foundation models from SAP Generative AI Hub or Azure Open AI. And deploying the RAG application using SAP CAP. We have provided all the repositories as part of the mission and here is the link to our Discovery Mission: Harnessing Generative AI Capabilities with SAP HANA Cloud Vector Engine

In this blog series, I will discuss the architecture and detail one of those scenarios in the follow-up blogs. For the remaining scenarios, I encourage you to subscribe to the Discovery Mission to gain access to Git repositories and Python scripts.

What is the ideal use case for this Discovery Mission?

Consider a hypothetical healthcare client, referred to as Client X, who has data stored across multiple systems, including SAP, Salesforce, and external platforms. This data could consist of customer interactions such as calls or emails to their call centers, which could be inquiries, service requests, or feedback about the services offered by the healthcare company.

Client X is interested in leveraging SAP HANA Cloud to process this unstructured data, enabling their business to run direct queries on feedback or transcribed phone calls. This enriched information can then be used for reporting purposes. For instance, if the business asks, "Display all service requests from the past two weeks," the system should be capable of scanning all transcribed texts, analyzing the content to distinguish between service requests and feedback, and delivering the relevant customer texts for the business to act upon. This scenario provides a clear illustration of how:

SAP HANA Cloud can access multiple Large Language Models (LLMs) from the SAP Generative AI Hub (GenAI Hub) and embed the transcribed texts.
The SAP GenAI Hub SDKs enable seamless access to LLMs.
SAP HANA Cloud can access JSON documents either from a Data Lake or from JSON Document Collections within SAP HANA Cloud itself.
The SAP Langchain plugin aids in the ingestion of embedded data into SAP HANA Cloud.
The Vector Engine can be leveraged to query the embedded texts in SAP HANA Cloud based on user prompts.

While we cannot provide actual customer data due to privacy concerns, we will substitute it with JSON reviews about various products or restaurants that will demonstrate all the points mentioned above. We have included a schema as part of the Discovery Mission, and we will also share some scenarios and code snippets in our upcoming blogs.

What about the architecture for the use case leveraging SAP HANA Cloud Vector Engine and Large Language Models(LLMs) from Generative AI Hub?

The architecture we're discussing consists of two main phases: data ingestion and user interaction. Let's dive into the details:

Phase 1: Data Ingestion (Steps 1-3) & Phase 2: User Interaction (Steps 4-8)

We kick things off by reading customer reviews from an Azure data lake and ingesting the data as JSON documents into our system. We'll provide the schema for these JSON documents as part of this Discovery Mission but will access the Data Lake as part of the blog series and walk you through the code.
Next, we leverage the power of the GenAI Hub SDKs and the langchain plugin to read the JSON documents from SAP HANA Cloud and embed them using text-embedding-ada-002 (LLMs) from Generative AI Hub.
With the embeddings obtained, we ingest the data back into SAP HANA Cloud using the langchain plugin, setting the stage for the next phase.
Users can now interact with the deployed CAP app by prompting it with queries related to restaurant reviews.
The app utilizes the destination service to connect to LLMs from the GenAI Hub and embeds the user's prompt.
This embedded prompt is then queried against the vectors ingested in Step 3, allowing us to retrieve relevant information.
Before presenting the results to the user, we access the tiiuae--falcon-40b-instruct(LLMs)from the GenAI Hub to analyze the sentiment of the retrieved text, adding an extra layer of context.
Finally, the app responds to the user's prompt with the actual text, along with its relevance score and sentiment analysis, providing a comprehensive and insightful answer.

What about the architecture for the use case leveraging SAP HANA Cloud Vector Engine and Large Language Models(LLMs) from Azure Open AI?

We adopt a similar approach as discussed before.

Phase 1: Data Ingestion (Steps 1-3) & Phase 2: User Interaction (Steps 4-8)

Our journey begins by connecting to an Azure data lake to read customer reviews. These reviews are then ingested as JSON documents into our system. We'll provide the schema for these JSON documents as part of our mission, but we'll also delve into the code in this blog series for a more detailed understanding.
Next, we harness the capabilities of Azure Open AI SDKs and the langchain plugin. These tools allow us to read the JSON documents from SAP HANA Cloud and embed them using text-embedding-ada-002 (LLMs) from Azure OpenAI.
After extracting valuable embeddings from the JSON documents, we'll ingest this enriched data back into SAP HANA Cloud. The langchain plugin streamlines this process, ensuring a seamless integration between Azure OpenAI and SAP HANA Cloud.
At this stage, users can interact with the deployed Cloud Application Programming (CAP) app, prompting it with queries about product reviews.
When a user submits a prompt through the CAP app, the app leverages the destination service to connect with Azure OpenAI's LLMs. These LLMs then embed the user's prompt, transforming it into a format suitable for efficient querying.
The embedded user prompt is then queried against the vectors ingested in Step 3. This process enables us to identify relevant customer reviews that closely match the user's query.
Based on the query results, we retrieve the corresponding text, along with its scoring and sentiment analysis. To ensure accurate sentiment analysis, we access the text-davinci-003 (LLM) from Azure OpenAI's deployment.
Finally, we synthesize the retrieved text, scoring, and sentiment analysis into a coherent response tailored to the user's prompt.

Throughout the following blog series, we'll dive deeper into the code implementation, guiding you through each step of this exciting journey. Stay tuned for more insights and practical examples!

What configurations & pre-requisites you need to navigate through this Discovery

Mission?

To ensure you're ready to execute these steps, please refer to the "Preparation" and "Setup" sections of the Project board, included in the Discovery Mission.

If you don't have a subscription for GenAI Hub, don't worry.

We've provided alternative options using Azure OpenAI to keep you moving forward.

What about the essential Repositories and Scripts?

In our mission, we explored diverse scenarios to showcase the capabilities of SAP HANA Cloud Vector Engine and Generative AI Hub. For those experts eager to delve deeper into these technologies, we've provided Python scripts that serve as a practical resource. The other scenarios are catering to experts focused on SAP HANA Cloud Vector Engine and Azure OpenAI/OpenAI integration.

Here's a breakdown of the scenarios:

Scenarios 1 through 4 introduced various Python scripts that demonstrated the core functionalities of embedding using different SDKs & plugins. Building upon the previous scenarios, Scenarios 5 and 6 focused on validating the embeddings generated from Scenarios 1 to 4. These validation steps ensured the accuracy and reliability of the embeddings, which are crucial for downstream tasks like similarity analysis and clustering.

Finally, Scenario 7 showcased an SAP CAP application that validated embeddings based on either SAP Generative AI Hub or Azure OpenAI/OpenAI integration. This real-world application demonstrated how these technologies can be seamlessly integrated into existing workflows and applications.

Throughout these scenarios, we aimed to provide a comprehensive overview of SAP HANA Cloud Vector Engine and Generative AI Hub, equipping experts with the tools and knowledge necessary to leverage these powerful technologies effectively.

If you have BTP subscriptions to both SAP HANA Cloud and SAP Generative AI Hub, you have the capability to utilize the "Python Scripts-GenAI Hub" tile. This feature enables you to import data from provided JSON document samples and cross-check the data using the Python scripts that are part of these tiles.

For those with subscriptions to SAP HANA Cloud and Azure Open AI, you have the ability to run the "Python Scripts-Azure Open AI" tile. This feature lets you import data from sample JSON documents and subsequently verify its accuracy.

How do you deploy the SAP CAP Application?

You can easily set it up by adhering to the step-by-step instructions provided within this tile. It's worth noting that the CAP application is compatible with LLMs based on both SAP Generative AI Hub and Azure Open AI.

In the upcoming blog, we'll dive into a practical, hands-on exploration and code review for one of the scenarios we've discussed.

We encourage you to delve into the mission and follow the step-by-step content to gain a deeper understanding of the SAP HANA Cloud Vector Engine and its impressive features that you can experiment with. Your feedback is highly anticipated and greatly valued. If you encounter any difficulties while navigating through the mission, don't hesitate to contact our support team through the Discover Center Mission. Here's to an enjoyable and enlightening learning journey!

Demystifying Transformers and Embeddings: Some GenAI Concepts

2024-03-28T21:07:49.791000+01:00

Introduction:

Generative AI (GenAI) keeps generating so much news and so many innovative applications in many domains and industries. The potential impact of GenAI on businesses and society is becoming difficult to deny. Thought leaders have compared the impact of GenAI to the invention of the transistor, the internet, and even FIRE.

Being at SAP part of the Partner Ecosystem Success group, I had the opportunity to talk to partners and customers, testifying that Business AI is first on the agenda, as SAP's CEO Christian K. and Walter S., Head of AI at SAP, expressed in this short video.

SAP is at the forefront of this new enterprise revolution and rightfully so, as SAP applications constitute the digital platform of business in the world, where the oil of Gen Business AI being the business Data is being generated and transformed. As the saying goes, with great power comes great responsibility; therefore, SAP is strategically focusing on Relevant, Reliable, and Responsible AI.

On the GenAI technology news from SAP, the following are the latest:

Generative AI hub: This is the place where SAP and its ecosystem will integrate LLMs and AI into new business processes in a cost-efficient manner.

Joule: The Generative Business AI Assistant from SAP. This will become the main UX for SAP Applications like S/4HANA Cloud and SuccessFactors HCM, providing information retrieval, navigational assistance, transitional assistance, and analytics assistance with ad-hoc and natural language querying capabilities.

SAP Build Code: This is where the developer community will build new user experiences with such speed of development thanks to embedded productivity tools powered with GenAI.

SAP HANA Database Vector Engine: This is the in-memory database from SAP, integrating under one single APIs set all the required engines for transactional workloads, Analytics/ML, and now GenAI thanks to the added support for a Vector engine. The Vector engine is the technical component used for managing Embeddings and using them to ground GenAI business use cases. Grounding is a term used in the context of RAG (Retrieval-Augmented Generation) to condition the LLM with the relevant context and therefore limit hallucinations.

Understanding Transformers and Embeddings:

Talking to partners and customers about GenAI is fun, but for more productive brainstorming, it's sometimes important to level set on the technical side with some GenAI basics, such as Embeddings and Transformers.

There are many great blogs and videos explaining these topics in detail using GPT-2, so this is a great resource as most LLMs use a very similar architecture. The resources that helped me most and seemed to be the best for people with technical background are from Niels Rogge from HuggingFace:

Transformers like GPT act on tokens. Tokens are sub-words, and all the LLMs sub-words is the vocabulary. GPT-2 has a vocabulary size of 50,257 tokens, with each token represented by an Embedding of 768 dimensions. In comparison, GPT-4 seems to have 100,256 tokens and a default embedding vector of 1,536 dimensions (GPT-4 has a higher size of 3,072 dimensions and comes with the text-embedding-3-large model).

The tokens are produced with a tokenization ML process that tries to maximize the compression of language into an adequate vocabulary size. In GPT, the Embeddings are encoded into vectors using a training phase through the Transformer (see the 2nd from Niels).

You can experiment with tokenization on OpenAI here. For example, "hello world, my name is" will produce these 6 tokens: ['hello'- ' world' - ',' - ' my' - ' name' - ' is']. In French, when translated literally to "bonjour monde, mon nom est," it produces 7 tokens. Less common words will produce more tokens; therefore - fun fact - non-English languages will tend to produce more tokens, i.e., higher dollar cost for the same number of language words of a given prompt.

One forward pass through the Transformer architecture of GPT-2, i.e., predicting one next token, can be summarized in Python as follows. The code is self-explanatory with detailed inline comments.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the GPT-2 tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")

# Prompt
prompt = "hello, my name is" 

# Tokenize the input text and return their IDs
input_ids = tokenizer(prompt, return_tensors="pt").input_ids

# Pass the tokenized input through the GPT-2 model to get the logits (Unnormalized embeddings) representing the likelihood scores of each possible next word.
logits = model(input_ids).logits

# slices the output to get the logits only for the last position in the sequence, as we're predicting the very next word
next_token_logits = logits[:, -1, :]

# finds the index (ID) of the word with the highest probability score.
next_token_id = torch.argmax(next_token_logits, dim=-1)

# Decode i.e. convert the numerical token ID back into its corresponding word.
next_token = tokenizer.decode(next_token_id[0])

# Print the predicted next token
print(f"Next token is {next_token}")

When running this program on a Jupiter Notebook or Visual Studio Code, the next keyword will be "John." As a developer, you can add a loop to keep appending the next_token to the end of the prompt and perform a forward pass to see what the following tokens will be.

The videos above from Niels are very informative but could be a bit technical for some community members. To make it easier to explain, I spent a lot of time thinking about how to demonstrate the concepts without resorting to the phrase “Some magic happens here…”. Here is what I came up with, hoping it will help you understand and explain broader GenAI topics to your partners and customers.

Imagine a 3-dimensional space representing some concepts like Fruits, Programming languages, and Software companies.

The following matrix with the name "embedding" is the algebraic representation of such a universe. The labels set is the vocabulary, and the X, Y, and Z coordinates are the corresponding embedding vectors. Please continue through the Python code, as it's purposefully well-documented to be self-explanatory.

import numpy as np
import random

# Pre-defined (random) word embeddings
embeddings = {
    # Fruits I love
    "Apple":        np.array([0.85,          0.10,       0]),
    "Banana":       np.array([0.91,          0.05,       0]),
    "Orange":       np.array([0.90,          0.06,       0]),
    "Figs":         np.array([0.88,          0.10,       0]),
    # Programming Languages I know
    "Java":         np.array([0.10,          0,          0.85]),
    "JavaScript":   np.array([0.05,          0,          0.91]),
    "Python":       np.array([0.06,          0,          0.90]),
    "ABAP":         np.array([0.10,          0,          0.88]),
    # Enterprise software companies :)
    "SAP":          np.array([0,             0.85,       0.10]),
    "Oracle":       np.array([0,             0.91,       0.05]),
    "Microsoft":    np.array([0,             0.90,       0.06]),
    "Salesforce":   np.array([0,             0.88,       0.10]),
}

# Prompt 
prompt = "I love ABAP and"

# Split the prompt into individual words
prompt_words = prompt.split()

# Function to get embedding for a word
# Return a vector of ones (identity vector) if the word is not in the vocabulary
# Since 'I', 'love, 'and' are not in our vocabulary, it will get an embedding of [1, 1, 1]
def get_embedding(word):
    return embeddings.get(word, np.ones(3))

# Calculate the average embedding of the prompt
prompt_embedding = np.mean([get_embedding(word) for word in prompt_words], axis=0)

# Get our vocabulary 
vocabulary = list(embeddings.keys())  
# Remove words in the prompt from the vocabulary so that they won't be used to complete the prompt
filtered_vocabulary = [word for word in vocabulary if word not in prompt_words]

# Calculate dot products between the prompt embedding and the remaining vocabulary embeddings
dot_products = {word: np.dot(prompt_embedding, get_embedding(word)) for word in filtered_vocabulary}
# Add some randomness in the chosen next word  Sort the words by their dot products and select the top two
top_two_words = sorted(dot_products, key=dot_products.get, reverse=True)[:2]
# Randomly select one of the two highest words. 
predicted_word = random.choice(top_two_words)

print(f"Predicted next word: {predicted_word}")

When running this code on a Jupiter Notebook or Visual Studio Code, you will be able to complete the prompts with the next programming language that I love 🙂 I you type "I like Apple and" it will complete with another fruit. Go ahead and experiment with you own categories and I hope this simulation can help you appreciate the scale of a real LLM like GPT-4 Turbo that acts on a vocabulary of 100,256 tokens and Embedding vectors of 3072 dimensions!!!

This simple code can predict the next word, classify words, etc. from the given vocabulary.

Conclusion:

To be clear, this is a very simplistic illustration of how high school algebra, i.e., matrix multiplication, is at the centre of today's technology revolution. Among other things, the Transformer neural networks like GPUs/TPUs/LPUs for their great capabilities in matrix operations and parallelization of such workloads. It's important not to underestimate other concepts in the Transformer architecture, such as tokenization, encoding, embedding, attention, and the neural network architecture of the Transformers, as well as Reinforcement Learning, Alignment, etc.

Understanding the technical concepts behind GenAI, such as Transformers and Embeddings, is crucial for productive brainstorming and discussions on GenAI applications in various domains and industries. This blog post aimed to provide a primer on these concepts, encouraging readers to explore the provided resources and code examples to deepen their understanding.

First hand experience at SAP Build Code - Learn, Try it out, Get a badge, SAP Discovery Centre

2024-03-29T08:36:58.864000+01:00

With new advancements in SAP Business AI, I am glad to reiterate the messaging from SAP on the General Availability(GA) of SAP Build Code.

I will cover about:

SAP Build Code
SAP Help Portal - covering initial setup for productive use, administration, security aspects etc
TD tutorial on SAP Developer Tutorial
SAP Learning Journey to get a badge
Pricing in SAP Discovery Centre and different service plans(Standard Plan, Free Plan and Trial)
Setup SAP Build Code on Trial

What is GA?

For those who are new to such acronyms like GA, TD etc. for now keep a note, GA stands for General Availability, which refers to the availability of the product for general use - this product will be loved by developers though!

And Test Drive(TD) refers to the availability of this service for developers to try and evaluate the service for a stipulated period of time to get a feel/before turning to a paid plan. What is this stipulate period of time - you should refer to the different plans of SAP Build Code on SAP Help portal.

What is SAP Build Code?

Since the discussions on SAP Business AI emerged and announcements were made during SAP TechEd 2023 about a new AI-based code generation offering in SAP BTP to facilitate code development, developers were waiting for the GA of SAP Build Code in Q1 2024. And there comes the announcement from @bhagat_nainani here.

Now customers can use SAP Build Code for productive developments. But what is it and what does it offer?

SAP Build Code is a service on SAP Business Technology Platform (BTP) similar to other services like SAP Build Apps, SAP Build Process Automation etc.

It offers an AI-powered cloud development environment through Joule copilot and leverages ready-to-use SAP services to foster the development of SAP UI5, SAP Fiori and Cloud Application Programming applications running on SAP BTP using the Cloud Foundry runtime.

The below pictures depicts its need:

Source: IDC, Market Analysis Perspective: Worldwide Modern and Edge Application Development and Developer Trends 2023, Doc #US50135723, September 2023

Who should use it?

It is designed to provide cloud based development environment suitable for both citizen and professional developers within the SAP ecosystem following best practises.

Let's review some of its key features

End to End Application Lifecycle management
- This development environment is designed to facilitate coding, testing, integration, deployment and also to maintain entire application lifecycle management. Once you enter this service, you can come out with a full fledged application deployed 🙂
Harnessing power of AI - Joule
- SAP Build Code harnesses the power of AI to offer developers efficient and accelerated assistance in building their applications. With advanced features like code completion, debugging, and code generation, this AI-based tool facilitates faster and more streamlined application development.
Keep core clean
- Another very important feature of SAP Build Code is that it helps to keep your core ERP systems viz. S/4HANA free from customizations following a clean core strategy. This implies, SAP BTP offers a platform for creating applications and building extensions.

Flexibility and BYOL
- Being a cloud service, you have a flexibility to work from anywhere, anytime and also, gives flexibility for developers to use any programming language of their choice.

Pre-built components and Templates
- Similar to the No-Code development service like SAP Build Apps, SAP Build Code comes with a variety of pre-built components like UI Elements, Business Logic and Integration connectors. It also offers templates, which acts as a starting point for their application development thereby boosting developer efficiency.

Access to data and integration
- As SAP Build Code is based on SAP BTP, hence giving its developers an easy access to SAP data and services. It also includes pre-built integrations, APIs, and connectors to SAP and non-SAP systems with built-in SAP developer best practices to accelerate development.

Most importantly all of this via a visual programming tools and guided wizards of SAP Build Code.

Source: SAP Learning Journey on SAP Build Code

Source: SAP Learning Journey on SAP Build Code

As you can see from the above diagram SAP Build Code offers a bundle of:

Application Services (viz CAP Model, UI Development, Mobile Services and Document Management) and
Runtime Service (viz. CI/CD, Authorization and trust management, Destination, Connectivity, Cloud logging, Alert Notification and many more .

In a nutshell, this service allows developers to seamlessly integrate multiple programming languages and low-code approaches in their projects and have effective collaboration between business users and developers.

Where do I find more about SAP Build Code? SAP Discovery Center and SAP Help Portal

To know about SAP Build Code service availability, region, pricing - kindly refer SAP Discovery Center.
Different Service plans of SAP Build Code can be referred here.
You should refer to the SAP Help Portal to explore more about SAP Build Code, Initial Setup, Development, Administration and Security.

Source: SAP Discovery Center - SAP Build Code

Kindly note, as of now, SAP Build Code is GA for 2 regions Europe (Frankfurt), US East (VA) and on AWS. Kindly look at your SAP BTP sub account region and AWS data centre. This information will keep changing and will be updated.

Setup SAP Build Code in your SAP BTP subaccount - refer here

If you are looking to get started on creating your first project - refer here

having questions related to SAP Build Code Security - refer here

Setup SAP Build Code on SAP BTP Trial Account

Well the purpose at this point in time to share a demonstration of how did I start with the Test Drive(TD) tutorial from SAP and subscribed to SAP Build Code on a trial account.

Access the Test Drive(TD) tutorial here SAP Build Code Test Drive

Followed by completing a learning journey from SAP Learning Journey platform till getting a badge:

Access the SAP Learning Journey on creating applications and extensions using SAP Build Code here

I don't have to rewrite the entire developer tutorial on SAP Build Code, the author of the TD tutorial have done a wonderful job already, but the feel of developing a full stack application within an hour was something that excited me.

I spent some time in recording, editing it to remove noise and wait time to give you a feel of how you can have your first hand experience too. Now it is time for me to try more use cases having the understanding of the service.

In the meantime, I recommend you having a look at the video 😊 Kindly do not feel obligated to to subscribe to like or comment - I just uploaded it on the platform for you to view this video.

Keeping up-to-date

I strongly suggest you to go through some of the first announcements from SAP and also through a lot of blogs from SAP Community. Do look out for more information on SAP Help Portal, about what's new in SAP Build Code, SAP Community and feel free to connect with me on social media channels like LinkedIn - you will see a flood of updates on SAP Build Code along with tsunami of tutorials, live streaming sessions and much more 🙃

SAP website on SAP Build Code
SAP Learning Journey on SAP Build CodeSA
SAP Developer tutorial on how to setup SAP Build Code on SAP BTP Trial
SAP Discovery Centre for pricing of SAP Build Code
Announcement by @bhagat_nainani on General Availability of SAP Build Code to speed up development
Blog from @qmacro on Test Drive of SAP Build Code
Update on how SAP Build Code can be tried in BTP Trial account in the blog SAP Build Code on SAP BTP Trial

Feel free to post your questions on SAP Community and do join a special live streaming episode by SAP

AI Foundation on SAP BTP: Q1 2024 Release Highlights

2024-04-05T09:47:35.574000+02:00

AI Foundation is SAP’s all-in-one AI toolkit, offering developers AI that’s ready-to-use, customizable, grounded in business data, and supported by leading generative AI foundation models. It is also the basis for AI capabilities that SAP embeds across its portfolio.

If you are not already familiar with the different capabilities of the AI Foundation stack represented below, read this introductory blog post.

Let's delve into the Q1 2024 release highlights of the AI Foundation, including innovations for the Document Information Extraction service, SAP Translation Hub, SAP AI Core and SAP HANA Cloud.

AI Services

Document Processing - Document Information Extraction

New: Combine Different Setup Types When Adding Data Fields to Schemas

Users can now combine header fields with different setup types in the same schema.
They can add header fields with the following setup types to a schema created for a standard document type (e.g. invoice) or custom document type:

auto (with and without a default extractor)
manual

This extends the scope of the existing standard schemas from SAP, such as for the invoice document and reduce time to value for new business fields (key-value pairs) that need to be extracted from documents.

New: Conversion of Country Specific Unit of Measure Values to ISO Format

The conversion of country specific unit of measure values into ISO format for invoice documents has been improved. For instance, the processing of certain locale-specific unit of measures (e.g. German 'Stk.' for 'Stück' / 'piece'). Users can expect improved quality of extraction and faster business process execution.

New: Purchase order number extraction from line-item level

Users can extract purchase order numbers that are available on-line item field level from invoice documents. It allows a faster execution of accounts payable processes when there are multiple PO numbers listed in the tables of supplier invoices.

New: New Invoice Supported Language – Japanese

The Document Information Extraction service supports now the Japanese language for invoice documents, improving global coverage.

New: Better Models for the Extraction of Standard Document Types

The machine learning models for the extraction of invoice, paymentAdvice, and purchaseOrder documents have been improved. Users can expect improvements in particular when extracting dates, amounts, tax ID, bank accounts.

Invoices: higher extraction accuracy can be expected for Japan, Hungary, Türkiye, and Romania.
Purchase orders: improved extraction accuracy can be expected for Spanish purchase orders.
Payment advice: more consistent column extraction and improved extraction of amounts in line-items.

Get started with Document Information Extraction.

Machine Translation - SAP Translation Hub

New: User settings for the document translation UI

Users can define the preferred UI language and theme in the application, which will be carried over across software and document translation UIs, thus improving the overall user experience. They can also define the preferred source and target languages to translate content using the application, which will apply only while using the document translation UI. It automates the translation process by not having to define the source and target language every time the application opens.

New: Updated tile in SAP BTP

As part of the efforts of migrating the application for software translation to the multi-cloud environment, the tile available on SAP Business Technology Platform has been renamed, from Document Translation to SAP Translation Hub. That’s one of the last steps towards completing the migration planned in Q2 2024.

Get started with SAP Translation Hub.

Generative AI Management & AI Workload Management

SAP AI Core

New: Availability of additional large language models in the generative AI hub

Integration of additional large language models (LLM):

Google PaLM 2 for text (text-bison), PaLM 2 for chat (chat-bison), and embeddings for text (textembedding-gecko)
Google Gemini Pro
Updates to Microsoft Azure OpenAI model versions

You can now leverage a greater selection of "best-to-fit" LLMs for your use case, ease exploration over market-leading generative AI models, without the need to go through lengthy contractual, legal discussions and finally harmonize lifecycle management across models.

Find more information here.

New: SDK for support of large language models

We introduced a series of features in SAP AI Core:

Technical libraries that simplify inference on large language models (LLMs) by automatically injecting the correct headers and paths into each request. It improves the extensibility, allowing users to add additional adaptations as needed.
Tooling for the effective integration and use of LLMs with LangChain in the context of the generative AI hub. It simplifies the developer experience with ready-to-use libraries for access to LLMs deployed using the generative AI hub.
A new library, the ai-core-llm-sdk, in addition to enhancements to the existing ai-core-sdk, to accommodate the required changes to support LLM access. It boosts efficiency when working with various LLM models by streamlining the deployment of LLM models and the querying of available models.

Get started with SAP AI Core.

Business Data & Context

SAP HANA Cloud

New: Support for storage and retrieval of vector embeddings in SAP HANA Cloud, called SAP HANA Cloud vector engine

A vector datastore manages unstructured data - such as text, images, or audio - in high-dimensional vector space as vector embeddings, to provide long-term memory and better context to AI models. This makes it easy to find and retrieve similar objects quickly, for example, by asking a question using natural language. This both simplifies interactions with large language models (LLMs) and empowers developers to securely implement generative AI in applications.

SAP HANA Cloud vector engine can now natively store and search vector embeddings, which are numerical representations of objects, along with business data as part of its industry-leading multi-model processing capabilities to power intelligent data applications.

With these vector capabilities, SAP HANA Cloud will enable Retrieval Augmented Generation (RAG), facilitating the combination of LLMs with private business data. These applications learn and adapt to new information, enabling automated decision-making.

Key benefits of the SAP HANA Cloud vector engine include:

Multi-model: Users can unify all types of data into a single database to build innovative applications using an efficient data architecture and in-memory performance. By adding vector storage and processing to the same database already storing relational, graph, spatial, and even JSON data, application developers can create next-generation solutions that interact more naturally with the user.
Enhanced search and analysis: Businesses can now apply semantic and similarity search to business processes using documents like contracts, design specifications, and even service call notes.
Personalized recommendations: Users can benefit from an improved overall experience with more accurate and personalized suggestions.
Optimized large language models: The output of LLMs is augmented with more effective and contextual data.

To deep dive into the SAP HANA Cloud Vector Engine, read these blog posts by our experts:

Build business-ready AI applications with SAP and stay updated!

Leverage the AI Foundation capabilities by visiting the SAP Discovery Center. Compare and select the service that fits most to your business needs. 
Explore the AI Foundation roadmap to discover past and upcoming innovations.
Engage with our community of SAP experts through Q&A and blog posts.

See you next quarter for exciting innovations!

SAP Developer News, April 18th, 2024

2024-04-18T14:16:44.083000+02:00

SAP CodeJam European Roadshow

SAP CodeJam Event Calendar: https://community.sap.com/t5/sap-codejam/eb-p/codejam-events
SAP CodeJam Group: https://community.sap.com/t5/sap-codejam/gh-p/code-jam

SAP CodeTalk with Daniel Schlachter on the CAP plugin concept

The interview https://www.youtube.com/watch?v=cG-cMqAhqyQ
The Capire section on "CAP Plugins & Enhancements" https://cap.cloud.sap/docs/plugins/#cap-plugins-enhancements

SAP Build Developer Challenge Week 3 Announcement

SAP builders group discussion : https://community.sap.com/t5/sap-builders-discussions/april- citizen-developer-challenge-sap-build-apps-task-3/td-p/13672860

FioriConf 2024 and SAP CodeJam – SAP Fiori elements flexible programming model

FioriConf 2024: https://www.fioriconf.org/
SAP CodeJam topic list: https://community.sap.com/t5/sap-codejam-blog-posts/sap-codejam-topics/ba-p/221407
SAP CodeJam – SAP Fiori elements flexible programming model: https://github.com/SAP-samples/fiori-elements-fpm-exercises-codejam

Solution Experience Live Session: Deliver Real-World Results with SAP Business AI, April 30, 2024

Register: https://events.sap.com/live-session-sap-business-ai/en/home
SAP Business AI release highlights: https://community.sap.com/t5/technology-blogs-by-sap/deliver-real-world-results-with-sap-business-ai-q4-2023-amp-q1-2024-release/ba-p/13650291

CHAPTER TITLES

0:00 Intro
0:07 SAP CodeJam European Roadshow
1:04 SAP CodeTalk with Daniel Schlachter on the CAP plugin concept
2:16 SAP Build Developer Challenge Week 3 Announcement
3:00 FioriConf 2024 and SAP CodeJam – SAP Fiori elements flexible programming model
3:55 Solution Experience Live Session: Deliver Real-World Results with SAP Business AI, April 30, 2024

TRANSCRIPT

[Tom]
This is the SAP Developer News for the 18th of April, 2024. Live from Amsterdam. Live from Bucharest. Live from Leverkusen, Germany. Hey there, we're halfway through this year's SAP Code Jam European Roadshow. As the little intro there demonstrated, we've hit three cities already, we've got Paris, France, and Madrid, Spain still to go on this trip. We've had a wonderful time interacting with all the community members, developers out there, introducing them to ABAP cloud as well as the SAP cloud application programming model. And this is a great time to remind you that anybody can request and offer to host an SAP Code Jam, we'll do our best to accommodate and and make that possible. So if you'd like to see next time's road show come to an area near you, consider hosting a code jam in the future.

[DJ]
Recently, there's a new plugin concept that's been introduced in the SAP Cloud Application programming model. On the one hand, it allows the CAP team to build out new functions and features for CAP in a really beautifully modular way and it allows us as developers to consume those extensions and new features also in a really beautifully simple way, often only requiring one line in our projects. It also allows us in the community to contribute extensions to CAP as well. This is the CAP plugin concept and recently I got the opportunity to talk about the CAP plugin concept and learn more about it from one of the enablement team members Daniel Schlachter. It's in a code talk format so you can consume it in video form or in audio form in the code talk podcast. Check it out. The link is in the description.

[Shrini]
Hello, SAP Builders. We are already in week three of the developer challenge. And this time, we will be diving into the theming capabilities of SAP Build. Our very own developer advocate, Rekha, has prepared an exciting challenge that will walk you through all the different options available in the theme, style, and layout tabs of SAP Build apps. She has also provided step-by-step instructions on how to complete the challenge in a post. Participate in the challenge and share your screenshots, along with the hashed answers in the reply section of this discussion in the SAP Builders group. We are eager to see your responses to this challenge.

[Nico]
Hi everyone, and welcome to the SAP Developer News. This is just a friendly reminder that FioriConf 2024 happens on April 24th. It's free, live, online only, with a 100% focus on SAP Fiori. The event is organized by SAP community members and is a great fit for everyone working with SAP Fiori, be it developers, consultants, architects, or admins. Make sure to check out the link in the description. And speaking of SAP Fiori, we just added a new topic to the list of available topics for SAP Code Jam events, the SAP Fiori Elements Flexible Programming Model. Make sure to check out the link in the description if you want to host or attend an SAP Code Jam event, and also check out the repository with the content. Hope to see you soon. Bye.

[Nora]
Many partners and customers always ask me what SAP means by business AI, and what SAP's approach is to business AI, and how SAP can help them transform their businesses and leverage AI for their businesses. If you also have those questions, Then listen up because SAP Learning is hosting a 16-minute solution experience live session on the 30th of April at 1.30 Central European summertime to answer exactly those questions. The session is suitable for all audiences as it is a get started session. And you will not only learn about SAP's latest innovative use cases of AI, but also understand SAP's strategy towards business AI and how your business can benefit from it. The speaker is no other than Dr. Philipp Herzig, our Chief AI Officer, who will also guide you through the 2024 Q1 releases of Business AI and he will share all the know-how with you that you need to stay up to date and to transform your business. So make sure you register with the link below and sign up for the event and don't miss out on all the insights.

SIT Kolkata 2024 - An insightful Panel Discussion on SAP AI

2024-05-04T11:32:02.734000+02:00

With the increasing relevance of AI in recent years, it is no surprise how AI is slowly amalgamating with SAP and soon it will be an integral part of all business processes. That's what the great industry minds think or at least that's what my takeaway was from SAP Inside Track(SIT) KOLKATA 2024, the event that all SAP enthusiasts await throughout the year. The very first panel discussion of the event focused on the future of SAP AI and the following are the key-points I learned:

We are already adapting to BTP, which enables application development with the capabilities of AI. Predictive analytics and intelligent automation help improve efficiency by streamlining operations, reducing errors, accelerating time-to-market, optimising resource utilisation, identifying market trends and many more. Large amounts of data are available from the day-to-day business processes and exposing that data to machine learning, natural language processing, etc. can enhance ERP functionalities because Data is the new Oil and AI is the tool to harness it.
By combining Robotic Process Automation with cognitive capabilities, SAP can enable end-to-end automation of complex business processes, from invoice processing and data entry to Supply Chain Management and regulatory compliance. With the rise of GenAI, generative AI, the entire process of the Supply Chain is simplified with Prompt LLMS - alerting and recommendations based on the insights derived from warehouse data.
Not just business processes, SAP AI has extensive applications - waste management, optimised energy consumption, planned city development, improved customer experience, and risk management. It can even be utilised in the direction of public welfare. Banks and financial institutions utilize SAP AI to detect fraudulent activities, personalize financial advice, and automate customer service interactions. AI-powered video surveillance systems, and predictive policing by analyzing historical crime data, can enhance public safety.
Even as we embrace AI, some concerns remain about how we must strike the balance between the human factor and artificial intelligence, i.e., the goal is not to replace humans but to augment human capabilities for a smooth business flow and enhanced customer experience. As pointed out by our eminent panelists, the goal of AI should be to have a better human life and the final decision-making authority should only be that of a human, especially in cases where there's sensitive data involved like in the medical field. It must be ensured that SAP AI solutions adhere to ethical principles and comply with industry-specific, data privacy regulations.

As GenAI and SAP continue to evolve in tandem, it is our responsibility as SAP enthusiasts to keep learning and implementing the same for a better future with sustainable growth. Being part of the SAP community, it would be a great fallacy on my part to not thank our sponsors for contributing to the success of the event and our esteemed panel members for sharing their knowledge and ideas on SAP AI, the Future of AI Technology at SIT Kolkata 2024.

Panel members: Dr. Indranil Mitra, Partner PWC
Mr. Debasis Sen, CEO of New Bengal Consulting Pvt Ltd
Dr. Amlan Chakraborti, CEO of ICOE
Mr. Srini Gottimukkula, VP, SAP Labs
Mrs. Asidhara Lahiri, Enterprise Architect, SAP
Panel moderator: Dipanwita Dutta

@DataTherapist

Artificial Intelligence
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Your Ultimate Guide for SAP Sapphire 2024 Orlando

2024-05-13T18:17:33.473000+02:00

Your Ultimate Guide for SAP Sapphire 2024 Orlando:
Mark Your Calendars For These Must-Attend Sessions!

Join us at SAP Sapphire 2024 in Orlando from June 3rd to June 5th for an unforgettable experience filled with insights, innovations, and invaluable networking opportunities. As you prepare for this event, be sure to prioritize these sessions where you'll gain valuable insights into cutting-edge technologies and strategies that are shaping the future of customer support and business transformation.

Strategy Sessions:

SER242 : SAP runs SAP: Leading the AI revolution in customer support
Experience our AI-driven customer support paradigm. Uncover strategies for AI fusion, elevating support efficacy and customer satisfaction. Witness our journey, which sets benchmarks for AI-powered customer service excellence. Using this blueprint to smoothly integrate AI into your company, unlock success through the SAP for Me portal.
TRE322 : Transform Together: A holistic strategy to execution to bring out your best
With 70%+ of transformation projects at risk of failure, it takes more than technology to make successful strategy shifts. Accelerate your transformation journey through practices shared by SAP’s COO and CIO on holistic strategy to execution, process excellence, people-centered transformation, and innovation.

Artificial Intelligence Sessions:

SER207 + SER248: Translating Generative AI and LLMs into Business Benefits
Explore how generative AI and large language models (LLMs) are revolutionizing customer support, enabling engineers to provide lightning-fast assistance and enhance internal efficiencies. Elevate your support capabilities and accelerate your customer experience.

SER247 + SER202: Reinventing Support with AI
Discover how intelligent automation and AI are transforming customer support, delivering exceptional value and predictive insights. Step into the future of support and maximize your service experience.

SER203: Release the Value of AI in Your Business Transformation
Witness the transformative power of AI in business processes and learn how SAP Signavio solutions can guide your digital transformation journey. Explore the tangible value AI brings to your business.

ASUG307: AI in Customer Support: SAP's Path to Excellence
Gain insights into SAP's approach to infusing AI into customer support, enhancing efficiency, and elevating user satisfaction. Explore actionable tactics for seamless AI integration and effective measurement of its business outcomes.

SAP Cloud ALM Sessions:

SER201: Unlocking Growth: Air Liquide's Success with SAP Cloud ALM
Discover how SAP Cloud ALM empowers organizations to streamline business processes and drive growth. Learn from Air Liquide Advanced Materials' success story and gain valuable insights into maximizing your cloud journey.

SER200: Empowering Your Cloud Journey: Unleashing Transformation with SAP Cloud ALM
Dive into the transformation management approach with SAP Cloud ALM and explore integration possibilities with SAP Signavio, SAP LeanIX, and SAP Enable Now solutions.

SER227: Optimizing Innovation: Road Map for SAP Cloud ALM Solution
Gain insights into adopting clean core principles and explore the road map for SAP Cloud ALM. Learn how to drive business continuity and streamline operations through efficient integration.

SER204: SAP Cloud ALM for Operations at Brightspeed
Explore the value-driven benefits of SAP Cloud ALM through a case study on Brightspeed's operations. Learn how to leverage SAP Cloud ALM for business process monitoring, integration, and exception monitoring.

Clean Core Sessions:

SER253: Enable a Clean Core with SAP Cloud ALM
Discover SAP's strategy for RISE with SAP Migration and Modernization, focusing on SAP Cloud ALM as a platform for a clean core. Learn how to streamline complexity and preserve operational productivity.

SER258: Start Your SAP S/4HANA Cloud Journey Fresh with a Clean Core Strategy
If you've just signed up for SAP S/4HANA cloud subscription, this session is a must-attend. Explore the benefits of embedded launch activities and gain guidance for a smooth transition to cloud ERP.

Data Transformation Sessions:

SER256 + SER206: Migrating to SAP S/4HANA: Confident Data Transition
Unlock the value in transitioning from SAP ERP to SAP S/4HANA with the SAP Business Transformation Center solution. Accelerate your journey and take your business forward with confidence.

SAP for Me Sessions:

SER205: "SAP for Me": Bundles Unleashed
Discover the power of bundles in the "SAP for Me" portal and prioritize business-critical tasks. Empower your business with intuitive cloud service management.

SER257: SAP for Me: Reporting
Explore the capabilities of the Customer Insights Dashboard in the SAP for Me reporting application. Customize reports to meet your unique needs and gain a holistic view of your SAP solutions.

Don't miss out on these transformative sessions at SAP Sapphire Orlando 2024! Mark your calendars and prepare to be inspired.

Ensure you connect with our executives and experts at the Services and Support Center. Explore our expert and demo stations, or reserve 1:1 time with our specialists. Contact our reception to secure priority access and elevate your SAP experience with exclusive insights from our leaders and specialists.

See you there!