SAP has introduced a game-changing feature that addresses one of the most persistent pain points in AI implementation: the labor-intensive process of prompt engineering across different models.
The Problem That’s Been Holding Us Back
Picture this scenario: you’ve crafted the perfect prompt for your AI application using the Model A (ex. OpenAI chatGPT). Your system goes to production, users are happy, and everything runs smoothly.
Then Model B (ex. Google Gemini) launches with better performance and lower costs. And you want to switch to Model B.
The catch? Your carefully engineered prompts don’t work optimally with the new model. You will be faced with weeks of manual re-engineering, testing, and validation just to make the switch.
This model lock-in problem has been hidden. Teams often stick with suboptimal models simply because the cost of switching outweighs the benefits.
Enter SAP’s Prompt Optimizer: AI That Engineers AI
SAP’s new prompt optimizer in AI Foundation on SAP BTP fundamentally changes this equation.
Think of it as having an expert prompt engineer who instantly understands how to adapt your use case for any model in your toolkit.
The technology works by analyzing your existing prompts and automatically generating optimized versions tailored for different models.
This isn’t just translation—it’s intelligent adaptation that considers each model’s unique strengths, formatting preferences, and reasoning patterns.
Why This Partnership with Not Diamond Matters
The collaboration with Not Diamond brings crucial expertise to this solution. Not Diamond has built their reputation on model-agnostic approaches, meaning they understand the nuances of how different AI models process and respond to prompts.
This partnership ensures that SAP’s optimizer doesn’t just work with today’s models, but is designed to adapt to future AI innovations as they emerge.
The Bigger Picture: A Multi-Model Future
This development signals a maturation in how we think about enterprise AI architecture.
Rather than building systems around specific models, we’re moving toward model-agnostic infrastructures that can leverage the best tool for each job.
Consider the implications: Your customer service AI could use the most cost-effective model for routine inquiries while automatically switching to more sophisticated models for complex problems—all without manual intervention or prompt re-engineering.
Conclusion
The speed of AI innovation means new models emerge regularly, each with distinct advantages. SAP’s approach removes the friction that previously made adopting these improvements a significant undertaking.
The era of being married to your AI model choice appears to be ending, replaced by the flexibility to continuously optimize for performance, cost, and capability as the AI landscape evolves!
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In today’s fast-paced business world, AI isn’t just a buzzword—it’s a game-changer. SAP, a leader in enterprise software, is harnessing AI to transform how businesses operate. Let’s break down SAP Business AI in simple terms, focusing on its core principles, real-world impact, and ethical foundation.
What is SAP Business AI?
SAP Business AI integrates artificial intelligence directly into SAP’s cloud-based tools, making them smarter and more intuitive. It’s designed to enhance decision-making, automate tasks, and simplify complex processes—all while aligning with SAP’s three guiding principles: Relevance, Reliability, and Responsibility.
Understanding SAP’s Three-Pillar Approach to Business AI
1. Relevant AI That Solves Real Problems
Think of relevance in AI like having a conversation with someone who truly understands your job. SAP recognized that generic AI tools often miss the mark because they don’t understand the specific context of business processes. Instead of creating AI that exists in isolation, they embedded intelligence directly into the software systems that employees already use every day.
Consider how this works in practice with supply chain management. Rather than requiring workers to learn an entirely new AI system, SAP built intelligence into existing procurement workflows. When a delivery arrives, the AI can automatically read delivery notes, extract relevant data, and process invoices without human intervention. This approach has demonstrated cost reductions of up to 55% because it eliminates the friction of switching between systems and reduces manual data entry errors.
The human resources example illustrates another dimension of relevance. Generative AI doesn’t just speed up job description writing; it helps remove unconscious bias by suggesting more inclusive language and focusing on essential qualifications rather than unnecessary requirements that might inadvertently exclude qualified candidates.
At the center of this relevant AI approach sits Joule, SAP’s AI copilot. Rather than forcing users to navigate complex menu systems, Joule allows natural language interaction with business data. When you ask “Show me this quarter’s sales trends,” you’re not just getting a report – you’re engaging in a conversation that can drill down into specifics, compare different time periods, or highlight unusual patterns that might require attention.
2. Reliable AI Built on Solid Foundations
Reliability in enterprise AI means something different than reliability in consumer applications. When Netflix recommends the wrong movie, you might waste two hours. When business AI makes a mistake, it can affect supply chains, employee decisions, or financial reporting. SAP addresses this challenge through their AI Foundation, which serves as the technological backbone for all their AI capabilities.
This foundation combines decades of business process expertise with modern AI technology. Think of it like having an experienced business consultant working alongside a cutting-edge computer scientist. The business knowledge prevents the AI from making suggestions that sound technically correct but would be disastrous in practice, while the advanced technology ensures the system can handle complex, real-time data processing.
One crucial aspect of reliability involves preventing AI “hallucinations” – instances where AI systems generate plausible-sounding but completely incorrect information. SAP’s approach grounds their AI in actual business data and established processes, reducing the likelihood that the system will fabricate information or make recommendations based on incomplete understanding.
The always-available nature of the SAP AI Foundation means that reliability isn’t just about accuracy; it’s also about consistency. Business operations don’t pause for system maintenance or updates, so the underlying AI infrastructure must maintain performance even as it evolves and improves.
3. Responsible AI That Maintains Human Agency
Perhaps the most complex pillar involves ensuring that AI enhances rather than replaces human judgment. SAP’s approach to responsible AI recognizes that the most powerful AI systems are those that amplify human capabilities while maintaining transparency and accountability.
Human control remains paramount in this design. Rather than creating black-box systems that make decisions without explanation, SAP builds AI that shows its reasoning and allows users to review, modify, or override recommendations. This approach acknowledges that business context often includes factors that even sophisticated AI might miss – regulatory requirements, company culture, or strategic considerations that aren’t captured in historical data.
The fight against bias represents another crucial dimension of responsible AI. Machine learning systems often perpetuate or amplify biases present in training data. SAP addresses this by actively training their models to recognize and avoid discriminatory patterns, particularly in sensitive areas like hiring and performance evaluation.
Data privacy and regulatory compliance form the foundation of responsible AI implementation. With regulations like the EU AI Act establishing strict requirements for AI systems, SAP ensures that their AI capabilities meet these standards from the ground up rather than adding compliance as an afterthought.
Key Components of SAP Business AI
Apart from the 3 key characteristics of SAP Business AI highlighted above SAP Joule (SAP’s Generative AI copilot) and SAP AI Foundation is presented as significant elements of SAP Business AI.
Joule: Your Generative AI Copilot in Action
Joule isn’t just a chatbot—it’s a paradigm shift in user experience. Here’s how it transforms workflows:
Natural Language Interaction:
Ask: “Find suppliers with the best sustainability ratings.”
Command: “Create a project timeline for Q4 marketing campaigns.”
Context-Aware Insights: Joule understands your role, industry, and past actions to deliver personalized recommendations.
Cross-App Integration: Pull data from SAP S/4HANA, SuccessFactors, or Ariba seamlessly.
Real-World Use Cases:
A factory manager uses Joule to automate invoice processing, saving $300K/year.
An HR leader generates bias-free job postings in 2 minutes, reclaiming hours weekly.
The SAP AI Foundation: The Brains Behind the Brawn
This isn’t just technical jargon—the SAP AI Foundation is what makes everything possible:
Pre-Trained Business Models: SAP fine-tunes generic AI models (like GPT-4) with industry-specific data (e.g., supply chain or financial terms).
Scalability: Run AI workloads at enterprise scale, whether you’re processing 100 or 10 million transactions.
Flexibility: Choose from SAP’s own models, third-party LLMs, or build custom AI solutions.
Example: A retailer uses the SAP AI Foundation to predict holiday demand. The system analyzes historical sales data, weather trends, and supplier lead times—all via SAP BTP—to optimize inventory.
Why SAP’s Approach Stands Out
No Frankensteining: Unlike stitching together AI tools from multiple vendors, SAP Business AI is fully integrated with your ERP, HR, and supply chain systems.
Speed to Value: Joule and pre-built AI workflows mean businesses see ROI faster—no PhD in data science required.
Future-Proof: SAP continuously updates its AI models and ethical frameworks to align with regulations like the EU AI Act.
The Bottom Line
SAP Business AI represents a fundamental shift from standalone AI tools to integrated intelligence that works within existing business processes. By combining relevance, reliability, and responsibility.
SAP’s integrated ecosystem eliminates the friction that has plagued enterprise AI adoption. Joule’s ability to seamlessly connect S/4HANA, SuccessFactors, and Ariba data creates the kind of unified experience that enables peak performance rather than constant system-switching overhead.
Most importantly, SAP’s commitment to responsible AI ensures that as businesses automate more decision-making processes, they maintain the human values and ethical considerations that make organizations worth building. This isn’t just about regulatory compliance—it’s about amplifying human intelligence rather than replacing it.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In today’s rapidly evolving technological landscape, artificial intelligence has emerged as a transformative force across industries. Among the most promising developments in this field are AI agents—autonomous, intelligent systems that are reshaping how businesses operate. For SAP professionals and consultants, these AI agents represent not just a technological advancement but a strategic opportunity to drive unprecedented value.
Understanding AI Agents: Digital Colleagues, Not Just Tools
AI agents are far more than simple automation tools. Think of them as digital colleagues capable of independent action, learning, and adaptation. Unlike traditional software that follows fixed instructions, AI agents can understand objectives, formulate plans, and execute complex tasks with minimal human oversight.
Within the SAP ecosystem, these agents are breaking down traditional barriers between departments, creating seamless workflows, and enabling a level of operational intelligence previously unattainable.
The Cognitive Architecture of AI Agents
To appreciate the transformative potential of AI agents, we must understand their underlying architecture:
1. Natural Language Processing forms the foundation, allowing agents to understand and respond to human instructions through sophisticated large language models (LLMs). This enables SAP users to communicate with systems in conversational language rather than technical commands.
2. Planning and Adaptability capabilities enable agents to create custom workflows to achieve specified goals. Unlike rigid automation, these agents can recalibrate their approach when facing unexpected challenges or new information.
3. Tool Integration allows agents to access and leverage various software tools, databases, and APIs. In an SAP environment, this means seamless interaction across modules like S/4HANA, SuccessFactors, and Ariba.
4. Continuous Learning ensures that agents improve over time through both explicit feedback and observation of outcomes. Each interaction becomes a learning opportunity, making the agent increasingly valuable.
5. Collaborative Intelligence enables agents to work together in multi-agent systems, tackling complex problems that require coordination across different domains of expertise.
SAP Joule: SAP’s Answer to the AI Agent Revolution
At the forefront of SAP’s AI strategy stands Joule—an intelligent agent designed specifically for the SAP ecosystem. Joule represents a significant advancement in how businesses interact with their SAP environments through:
Context-Aware Intelligence: Joule understands the business context behind user requests, drawing from connected SAP systems to provide relevant, accurate responses.
Cross-Application Integration: By bridging traditionally siloed SAP applications, Joule creates a unified experience that transcends module boundaries.
Natural Conversation Capabilities: Users can interact with complex SAP systems through simple, conversational language, dramatically reducing the learning curve.
Continuous Evolution: Through ongoing interactions, Joule becomes increasingly attuned to user needs and organizational patterns.
Transformative Use Cases Across Business Functions
Finance: Accelerating Dispute Resolution
Financial teams often struggle with invoice disputes that create cascading problems: delayed payments, strained customer relationships, and distracted personnel. SAP’s AI agents address this challenge through:
Automated Case Validation: The moment a dispute arises, AI agents evaluate its legitimacy against historical patterns and established rules.
Intelligent Root Cause Analysis: Rather than surface-level assessment, agents conduct deep investigation across invoices, contracts, and communications to identify underlying issues.
AI-Generated Resolution Pathways: Instead of reactive responses, agents proactively propose resolution options with their likely outcomes.
Orchestrated End-to-End Resolution: Once a path is selected, agents coordinate all necessary activities—from issuing corrected invoices to updating records—creating a seamless experience for all parties.
The result is not just faster resolution but more accurate outcomes and improved customer relationships.
Human Resources: Reimagining Talent Management
The modern workforce demands responsive, personalized HR experiences. AI agents are making this possible by:
Streamlining Recruitment Cycles: HR managers can create requisitions and process candidates through natural language commands, reducing administrative burden.
Automating Routine Workflows: From generating job descriptions to processing time-off requests, agents handle predictable tasks with consistent quality.
Enhancing Employee Engagement: Managers gain immediate access to compensation insights and feedback mechanisms, creating more responsive leadership.
Empowering Self-Service: Employees can access information and initiate processes through conversation rather than navigating complex systems.
This transformation shifts HR from a primarily administrative function to a strategic partner in organizational success.
Sales: Converting Time into Customer Value
In sales environments, where time directly impacts revenue, AI agents create competitive advantage by:
Managing Sales Opportunities: Agents review opportunities, suggest responses to customer inquiries, and maintain current information without sales team intervention.
Eliminating Administrative Burden: Through natural language commands, sales professionals can update appointments, log meeting notes, and adjust quotes without interrupting their customer-focused activities.
Providing Contextual Intelligence: Before customer interactions, agents compile relevant insights from across the SAP landscape, ensuring sales professionals enter every conversation fully prepared.
Enabling Real-Time Forecasting: Sales pipelines adjust automatically based on current activities, creating more accurate projections without manual intervention.
These capabilities allow sales teams to focus on relationship-building rather than system management.
The Strategic Advantage of AI Agents
For SAP professionals, AI agents offer distinct advantages that extend beyond simple efficiency:
Elevated Focus: By handling routine tasks, agents allow professionals to concentrate on strategic, creative work that truly requires human judgment.
Enhanced Decision Quality: Agents provide comprehensive analysis and recommendations based on organizational data, leading to better-informed decisions.
System-Wide Optimization: Through multi-agent collaboration, organizations can coordinate activities across departments that traditionally operate in isolation.
Reduced Operational Costs: Automation of high-volume, repetitive tasks generates significant cost savings while improving consistency.
Scalable Operations: AI agents can manage increasing workloads without proportional increases in staffing, creating more resilient operations.
Data-Driven Intelligence: By continuously analyzing patterns across the organization, agents generate insights that might otherwise remain hidden in data silos.
Understanding the AI Agent Spectrum
Not all AI agents are created equal. SAP professionals should understand the range of agent types to deploy them effectively:
Reactive Agents respond to specific triggers with predefined actions—ideal for straightforward processes like access management or basic queries.
Proactive Agents anticipate needs by monitoring systems and identifying potential issues before they impact operations.
Hybrid Agents combine reactive and proactive capabilities to handle complex workflows that require both immediate response and forward planning.
Utility-Based Agents make decisions by evaluating potential outcomes against organizational priorities, optimizing for specific metrics like cost, time, or quality.
Learning Agents continuously refine their performance through feedback and observation, making them suitable for dynamic environments where conditions frequently change.
Collaborative Agents operate in coordinated systems to manage end-to-end processes that cross traditional functional boundaries.
AI Agents vs. AI Copilots: Partners, Not Competitors
While both leverage similar underlying technologies, AI agents and copilots serve distinct purposes:
AI agents operate autonomously, making decisions and taking actions with minimal human oversight. They’re ideal for predictable processes that require consistent execution.
AI copilots augment human capabilities by providing real-time assistance and recommendations. They shine in scenarios requiring nuanced judgment and creative thinking.
In a mature SAP environment, both have essential roles. Copilots enhance human performance in complex tasks, while agents handle routine operations independently.
Implementation Best Practices
To maximize the value of AI agents in SAP environments, consider these implementation principles:
Maintain Appropriate Oversight: Even as agents become more autonomous, establish clear governance defining when human approval is required.
Integrate Organizational Knowledge: Train agents on internal data and processes to ensure their actions align with organizational realities.
Foster Human-AI Collaboration: Create workflows where humans and AI agents complement each other’s strengths rather than operating in isolation.
Commit to Continuous Improvement: Regularly review agent performance and incorporate feedback to enhance capabilities over time.
Start With Well-Defined Use Cases: Build confidence through initial projects with clear boundaries before expanding to more complex scenarios.
The Future of SAP Professionals in an Agent-Enhanced World
As AI agents become more prevalent, the role of SAP professionals will evolve—not diminish. The most successful professionals will:
Develop expertise in designing and orchestrating agent-based systems
Focus on strategic business outcomes rather than tactical execution
Serve as translators between business needs and technical capabilities
Provide oversight and intervention for exceptional cases
Continuously identify new opportunities for agent deployment
Conclusion: Embracing the Agent-Assisted Future
The integration of AI agents into the SAP ecosystem represents one of the most significant advances in enterprise technology in decades. For SAP professionals, this evolution creates unprecedented opportunities to drive business value through intelligent automation, enhanced decision-making, and seamless cross-functional coordination.
As we move further into this agent-assisted future, those who embrace these technologies—understanding both their capabilities and limitations—will position themselves at the forefront of business innovation. The question is no longer whether AI agents will transform SAP environments but how quickly organizations can harness their transformative potential.
For SAP consultants and professionals navigating this changing landscape, continuous learning about AI agents isn’t just advantageous—it’s essential for future success.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In an exciting development for the SAP ecosystem, ABAP developers can now harness the power of artificial intelligence through SAP Joule, marking a significant milestone in the evolution of SAP development tools.
This integration brings ABAP development into the modern AI era, offering capabilities that were previously available only to developers working with other programming languages.
Understanding SAP Joule and Its ABAP Integration
SAP Joule has been serving developers across various programming languages as an AI-powered assistant, but its recent expansion to include ABAP support represents a particularly meaningful advancement.
ABAP, being the backbone of SAP’s ERP systems, has long awaited this integration. The wait has finally ended, with Joule now seamlessly incorporating into ABAP Development Tools (ADT) for Eclipse, bringing AI assistance directly into the development environment that ABAP developers use daily.
Three Pillars of Transformation
SAP has thoughtfully structured Joule’s ABAP capabilities around three fundamental areas that address different aspects of modern development challenges. Let’s explore each of these pillars in detail:
Acceleration Through AI
The first pillar focuses on accelerating development processes. Imagine having an intelligent partner that not only helps you write code faster but also ensures its quality. Joule achieves this through predictive code completion, automatically suggesting the next lines of code based on context and common patterns.
Furthermore, it can generate unit tests automatically, addressing one of the most time-consuming aspects of development while maintaining code quality.
Empowerment via ABAP AI SDK
The second pillar introduces something truly revolutionary: the ABAP AI SDK. This toolkit, powered by Intelligent Scenario Lifecycle Management (ISLM), opens up new possibilities for developers to integrate AI capabilities directly into their ABAP applications.
Developers can now connect their applications to SAP’s Generative AI Hub, enabling the creation of intelligent features within their ABAP projects. This represents a significant step forward in making ABAP applications more intelligent and capable.
Transformation Support
The third pillar addresses one of the most significant challenges facing many organizations today: the migration from classic ERP to SAP S/4HANA.
Joule’s future capabilities will include analyzing and simplifying legacy ABAP code, making this transition more manageable and efficient.
This support is crucial for organizations looking to modernize their SAP systems while maintaining the integrity of their existing processes.
Practical Implementation and Availability
For developers eager to start using these new capabilities, it’s important to note that Joule for ABAP is currently available across several platforms, including SAP BTP ABAP Environment, SAP S/4HANA Cloud Public Edition, and will be available in SAP S/4HANA Cloud Private Edition from the 2025 release.
The system is being offered with a promotional license that’s free until September 15, 2025, making it an excellent opportunity for teams to evaluate and integrate these capabilities into their development processes.
Looking Ahead
The introduction of Joule for ABAP represents more than just a new tool—it’s a fundamental shift in how ABAP development can be approached. By combining traditional ABAP development with AI capabilities, SAP is paving the way for more efficient, intelligent, and future-ready applications.
This integration promises to help developers write better code faster, while also preparing them for the challenges of modern enterprise software development.
The availability of AI-powered features like code explanation, predictive completion, and automated testing, combined with the ability to integrate AI capabilities into ABAP applications through the SDK, positions ABAP developers to create more sophisticated and intelligent solutions than ever before.
As organizations continue their digital transformation journeys, tools like Joule will become increasingly crucial in maintaining competitive advantage and delivering innovative solutions.
Getting Started
For ABAP developers interested in exploring these new capabilities, the path forward is clear: ensure your ABAP Development Tools for Eclipse are up to date, verify your licensing status, and begin experimenting with the new features.
The automatic configuration handled by SAP means you can focus on learning and implementing these new capabilities rather than dealing with complex setup procedures.
This new chapter in ABAP development represents an exciting opportunity for developers to enhance their productivity and expand their capabilities. Whether you’re working on new projects or maintaining existing systems, Joule’s AI-powered features can help you work more efficiently and effectively, ultimately delivering better results for your organization.
Excited to learn it through an end to end tutorial? Stay tuned for the future blogs!
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In a significant move to address the growing complexity of enterprise data management, SAP has unveiled its latest innovation: SAP Business Data Cloud (BDC). This comprehensive Software-as-a-Service (SaaS) solution marks a pivotal shift in how organizations can harness their data assets for AI-driven decision-making and business innovation.
Bridging the Enterprise Data Gap
Organizations today face a critical challenge: their valuable data often remains siloed across various systems, both SAP and non-SAP, limiting its potential impact on business decisions. SAP Business Data Cloud addresses this challenge head-on by offering a unified platform that brings together disparate data sources under a single, manageable ecosystem.
A Comprehensive Solution Built for the Future
The platform’s architecture is designed with several groundbreaking features that set it apart:
Unified Data Foundation
At its core, SAP Business Data Cloud serves as a centralized repository that consolidates enterprise data from multiple sources. This unified approach ensures that organizations can access, manage, and utilize their data assets more effectively, regardless of origin. The platform’s ability to maintain data quality while integrating diverse sources addresses one of the most persistent challenges in enterprise data management.
Advanced Analytics and AI Integration
Through its strategic partnership with Databricks, SAP Business Data Cloud incorporates sophisticated data engineering and machine learning capabilities. This integration enables organizations to: – Process complex data sets with advanced engineering tools – Develop and deploy machine learning models efficiently – Combine unstructured data with structured business data in a harmonized model
Innovation through Data Products
One of the most compelling aspects of SAP Business Data Cloud is its introduction of a data product economy. The platform delivers fully managed data products across various business processes, including: – Finance and spending data from SAP S/4HANA – Supply chain information from SAP Ariba – Learning and talent data from SAP SuccessFactors
These data products maintain their original business context and semantics, eliminating the need for costly extraction processes while ensuring data quality and relevance.
Enhancing AI Capabilities
The platform’s significance in the AI landscape cannot be overstated. SAP Business Data Cloud serves as a fundamental building block for enterprise AI initiatives by:
Strengthening Data Foundation for LLMs
A critical advancement in the platform is its ability to make enterprise data accessible to Large Language Models (LLMs). This is particularly significant because even the most sophisticated LLMs are only as effective as the data they can access. By providing a single source of truth, SAP Business Data Cloud ensures that AI models can learn from and utilize high-quality, consolidated enterprise data.
Empowering Joule AI Copilot
SAP’s generative AI copilot, Joule, receives a significant boost through Business Data Cloud. Powered by high-quality enterprise datasets and the SAP Knowledge Graph, Joule agents can now: – Understand end-to-end processes more effectively – Collaborate across different business functions – Address complex business challenges with greater accuracy
Looking Ahead
With its controlled general availability scheduled for Q1 2025, SAP Business Data Cloud represents more than just a new product launch—it’s a strategic enabler for organizations looking to accelerate their digital transformation journey.
By providing a unified platform for data management, analytics, and AI integration, SAP BDC positions itself as a crucial tool for organizations aiming to remain competitive in an increasingly data-driven business landscape.
The platform’s comprehensive approach to data management, combined with its advanced AI capabilities and strategic partnership with Databricks, makes it a compelling solution for enterprises looking to unlock the full potential of their data assets while preparing for an AI-driven future.
Stay tuned for a hands-on tutorial soon!
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
Have you ever wished you could simply ask your enterprise data questions and receive immediate, insightful answers? The wait is over! SAP is excited to announce the upcoming integration of SAP Analytics Cloud’s analytical capabilities into Joule, SAP AI-powered conversational interface.
This integration promises to transform how business users interact with their data, making it more accessible and actionable than ever before.
What is Analytical Insights in SAP Joule?
Joule, which can be enabled in various SAP applications such as SAP SuccessFactors and SAP S/4HANA, will now feature analytical capabilities powered by SAP Analytics Cloud.
This integration allows business users to explore data and gain insights through a natural, conversational experience.
Using familiar business terms, you can simply ask a question and Joule will instantly provide answers in the form of charts and metrics.
This is made possible by Joule’s multi-model architecture, which builds on the strengths of the Just Ask technology in SAP Analytics Cloud.
Joule acts as a single integration layer across the enterprise, connecting with both operational and analytical systems.
Key Benefits of the Integration
Conversational Analytics
Forget about complex data analysis tools. With Joule, you can interact with your data using natural language queries. Simply ask questions using familiar terms and receive answers in charts and metrics.
Seamless Integration
Access analytical insights directly within the SAP applications you’re already using. There’s no need to switch between applications, which reduces friction and saves time. You don’t have to sign in to SAP Analytics Cloud to get analytical insights, instead, you can get them from the context of your business application.
Powered by Just Ask
The analytical functionality is powered by the “Just Ask” feature of SAP Analytics Cloud. Data models indexed by “Just Ask” are what Joule uses.
Time and Efficiency Savings
The integration can cut the steps required to get analytical insights by up to 80%. This isn’t just about saving time, it’s about empowering your team to make data-driven decisions quickly.
Contextualisation
Get context-rich insights by combining both operational and analytical data within one platform. This will enable your team to make more informed decisions, faster.
Accessibility
Get insights anywhere via Joule instead of using dashboards in SAP Analytics Cloud Enterprise license. This means specifically tailored insights, easy to digest analytical information in the context of SAP applications used by your organisation.
How does it work?
Joule works with data from models indexed by the Just Ask feature of SAP Analytics Cloud.
The administrator for Just Ask in SAP Analytics Cloud determines which models and data are available to Joule. Joule translates natural language queries into real-time insights from SAP Analytics Cloud, with a deep integration with JustAsk. You can then have a conversation with your data until it gives you the insights you require.
Important Details and Restrictions
Controlled Release: The controlled release is planned for Q1 2025. You must be a customer of both SAP Analytics Cloud and a Joule-enabled SAP cloud application, both running on the SAP BTP Cloud Foundry environment to participate.
General Availability: General availability is planned for the Q2 2025 quarterly release. This will only be available for SAP Analytics Cloud tenants running on SAP BTP, Cloud Foundry environments.
Initial Restrictions:
Only acquired (imported) data models are supported initially. Live data models will be implemented in the future.
Analytical insights are delivered as metrics and charts.
The Joule client interface is supported in U.S. English only for the controlled release.
Supported browsers are Google Chrome and Microsoft Edge.
Joule cannot yet be enabled directly in SAP Analytics Cloud in this initial release.
User Access: Users need a valid account in SAP Analytics Cloud to access the analytical capabilities.
Data Control: The data available through Joule is determined by the administrator of the Just Ask feature within SAP Analytics Cloud.
Conclusion
The integration of SAP Analytics Cloud with Joule marks a significant step towards making data more accessible and actionable. By enabling conversational analytics within familiar applications, SAP aims to empower every employee to make faster, more informed decisions.
While the initial release has some limitations, these are planned to be addressed in subsequent updates, further enhancing the capabilities of this powerful integration.
This integration has the potential to transform how business users interact with their data, moving away from traditional dashboard-based analysis to a natural language based interaction.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
We know that SAP AI is no longer the distant future—it’s here, and it’s transforming how businesses operate, make decisions, and drive results. But what will 2025 hold for SAP AI and the companies that rely on it?
The story of SAP AI’s next chapter is not just one of innovation; it’s one of evolution. It’s about shifting from experimenting with AI to mastering it.
Let’s take a look at five key themes that will shape the AI landscape for SAP customers in 2025 and what they mean for the future of business.
The Rise of AI Agents: Moving from Assistants to Autonomous Agents
Today, we’re just scratching the surface of what AI agents can do. Sure, we have basic AI-driven search assistants, but 2025 is set to bring a new wave of more sophisticated multi-agent systems.
These AI agents won’t just be helping you search for documents or answering basic queries. They’ll be planning, reasoning, and collaborating—not just with SAP business users but with other systems too. Think of it like having an intelligent co-pilot by your side, capable of coordinating complex tasks across multiple systems. Fantastic isn’t?
What does this mean for businesses? It’s the potential for a future where AI is proactively responding to real-time business events—whether it’s a supply chain disruption or an unexpected surge in demand.
By 2025, AI won’t just help with tasks; it will predict and act in advance, allowing the SAP users to focus on strategy while the AI agents handle the repetitive, high-volume tasks.
Models: Quality Over Quantity
In the past, AI breakthroughs were driven by bigger models and more data. But 2025 will see a shift in focus. It’s no longer just about feeding massive amounts of data into models; it’s about giving AI the right data—data rich in context.
The truth is, context is everything. Without it, SAP AI is just guessing. But with context, AI becomes smarter, more accurate, and more useful.
The future of SAP AI will be shaped by how businesses leverage their own unique, rich data sources. The companies that win won’t necessarily be the ones with the biggest datasets, but those who can fine-tune their models to extract real value from their information.
Adoption: From Hype to Reality
By 2025, we’ll have moved past the hype of SAP AI. The real conversation will be about adoption at scale. Companies will no longer be asking, “Should we use AI?” Instead, they’ll be figuring out, “How do we use AI to solve our biggest problems?”
This shift will be driven by meaningful advancements in AI’s ability to integrate seamlessly with business processes. We’ll see AI working hand-in-hand with SAP business users, not just as a tool, but as a partner in achieving business outcomes.
AI as the New User Interface
What happens when AI becomes the primary way we interact with technology? In 2025, we’ll be closer to finding out. AI won’t just be behind the scenes, powering decisions—it’ll be front and center, changing how users interact with SAP software entirely.
Imagine a future where you don’t have to navigate a complicated interface or pull up endless dashboards to get what you need. Instead, you simply tell the system what you want, and the AI makes it happen.
In this new world, the user experience will be less about clicking buttons and more about communicating your intent. SAP AI will make systems simpler, more accessible, and ultimately more powerful.
Regulation: Navigating the New Rules of Innovation
AI is evolving faster than regulations can keep up, and 2025 will see an increasingly fragmented regulatory landscape. But rather than stifling innovation, regulation will provide an opportunity to shape the future of AI responsibly.
Businesses that succeed in the AI-driven world will be those that take ethics, safety, and responsible use seriously.
It’s not just about complying with the rules; it’s about setting the standard for what responsible AI looks like. The companies that build trust with their customers by using AI ethically will have a competitive advantage.
Looking Ahead: AI’s Impact on SAP Customers in 2025
As we look toward 2025, the future for AI is clear: deeper integration, AI agents, and new user interface. SAP AI won’t just be a tool; it’ll be a partner in driving business success. But success in this new era won’t come from doing more of the same. It’ll come from thinking differently about how we work, how we make decisions, and how we build our businesses.
As SAP customers, you’ll have the opportunity to lead this change. By embracing SAP AI in 2025, you’ll be setting the stage for a future where technology, SAP consultants and SAP business users work together to achieve more than ever before.
The question is, are you ready?
We at Zequance AI is helping SAP consultants to stand out from the crowd, and take career to the next level with our free SAP AI tutorials on youtube. Subscribe Now!
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In this blog, we’ll explore SAP Joule and understand how it helps businesses streamline processes, improve decision-making, and increase productivity of the business users.
SAP Joule is more than just an AI tool—it’s a game changer. It can provide real-time, contextual insights directly within SAP applications. Let’s dive deeper into its functionality, architecture, and its potential for business transformation.
What is SAP Joule?
SAP Joule is a generative AI assistant embedded across SAP’s enterprise software ecosystem. It acts as SAP’s AI copilot, allowing users to interact with SAP systems through natural language—just like you would chat with a colleague or friend.
You can ask SAP Joule to retrieve information, execute transactions, or provide insights, all within the context of your SAP environment, whether it’s SAP SuccessFactors or SAP S/4HANA Cloud.
SAP Joule is provided out of the box and natively integrated in SAP’s cloud applications. It comes with pre-configured content that is active right away for customers with limited configuration effort.
Currently, Joule is embedded in SAP cloud applications like SAP SuccessFactors and SAP S/4HANA Public and Private Cloud, and more applications to follow. However, keep in mind that Joule is available only for cloud solutions, not on-premises customers.
Why SAP Joule is a Game-Changer?
Remember the days when SAP users had to memorize complex Tcodes? Later, Fiori apps simplified things. Now, SAP Joule takes it to the next level.
So, what makes SAP Joule stand out?
Context awareness: Joule provides intelligent responses based on the user specific SAP transactions, and customer business data. It can maintain the context across different SAP products, whether you’re accessing Joule through SAP Start or directly within an application like SAP Successfactors.
Grounded on business documents: SAP Joule uses techniques like Retrieval Augmented Generation (RAG) to link to relevant sources, such as internal policies or business records, to provide accurate information and prevent AI from making things up.
Security and compliance: SAP Joule follows user authorization and authentication rules, keeping sensitive data safe and ensuring everything stays compliant.
Guardrails for responsible AI: It’s designed with safeguards against inappropriate use, including bias or hate speech.
Seamless Integration: Joule is built into the existing SAP environment, meaning it requires minimal setup and training. It integrates naturally with the systems you’re already using, making it highly accessible.
How Does SAP Joule Work?
Lets understand the high-level architecture of SAP Joule.
The user starts with their query in the Joule client in respective SAP application.
Each incoming request is processed based on 3 categories:
Scenario Catalog: Joule analyzes if the user-prompt is relevant for Joule’s Scenario Catalog. This catalog contains metadata of all available scenarios, functions, and skills of SAP cloud applications.
Knowledge Catalog: Then it conducts an informational filtering based on the Knowledge Catalog. This contains SAP-knowledge as well as the customer-owned knowledge. This process is based on Retrieval Augmented Generation (RAG).
Context: SAP Joule is aware of the user’s context and history. This information includes which SAP cloud application the user is using and which additional SAP license the customer has. Also, Joule is aware of the user’s role(s) and permissions. This means a user cannot access information or execute business processes which they are not authorized to do. Lastly, SAP Joule understands a user’s chat history and context.
Next, SAP Joule takes all of this information and gives a much richer query to the LLM which is responsible for the dialog management.
The LLM provides a grounded response back to the Joule service.
In this case, Joule calls the respective SAP backend system to proceed with the requested query, for example requesting for leave in successfactors.
The response is then filtered, and the customer gets a reply with full details that the conversation and output have been handled securely, with enterprise-level security and data privacy in place.
Great, now we understand that SAP Joule is an AI Copilot that’s integrated with SAP solutions to chat with it to retrieve information or perform tasks.
Use Case: AI Assisted Person Insights
An easy to understand use case of SAP Joule is the AI feature in SAP’s HR solution SAP SuccessFactors – AI-Assisted Person Insights.
As a manager, salary discussions can be one of the most challenging tasks. And, SAP SuccessFactors helps managers navigate these tough conversations with AI-assisted insights, ensuring they are well-prepared and confident.
For instance, SAP Joule, the AI co-pilot, helps managers review detailed insights on employees. It provides details about the employee’s compensation history, promotion record, bonus eligibility, and career development. The AI can even compare the compensation with market rates, making salary discussions more data-driven and balanced.
SAP Joule is much more than that, it can even help software developers write code and accelerate productivity using Generative AI. Let’s see a demo!
Demo
Let’s dive into a practical example of SAP Joule’s Generative AI and show you how to use it with SAP Build to develop a full-stack application.
By the end of this demo, you’ll know how to:
Create a full-stack project using a template,
Develop data entities and services with Joule’s Generative AI,
Generate application logic using Joule’s AI capabilities, and
Add a UI to the application and perform testing.
Watch the demo here:
So, What’s Next? Joule Extensibility
Many customers are asking to extend Joule’s capabilities. SAP is enabling users to create their own Joule AI skills via SAP Build, offering a low-code approach to developing custom skills.
Here are some exciting developments coming soon:
Joule Studio in SAP Build: Joule Studio is planned for Q1 2025. allowing users to create and manage their own skills. It is a new tool offering drag-and-drop simplicity for building, deploying, and managing custom skills.
Third-Party Integration: Joule will be able to integrate third-party systems, offering a fully integrated conversational experience.
Autonomous AI Agents: Soon, you’ll be able to build custom autonomous AI agents to solve complex workflows and collaborate with other agents in Joule.
Summary
SAP Joule is transforming the way businesses operate, offering AI-driven solutions that go beyond simple automation. SAP Joule is at the heart of SAP’s AI strategy, and as we move forward, its role will only become more critical in shaping the future of enterprise operations. Whether you’re in HR, finance, supply chain, or customer experience, Joule has something to offer that can radically change how you work.
Thank you, and we will see you in the next blog!
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
SAP AI uses various AI methods, including generative AI models to empower organizations to automate processes, enhance decision-making, and generate insights from massive datasets.
Generative AI plays a significant role in SAP AI’s capacity to enhance customer experiences, and develop intelligent assistants like SAP Joule.
In this blog, we will dive into the core components of Generative AI and how these models operate – allows us to not only discuss the latest advancements but also implement them effectively in real-world SAP environments.
How Does Generative AI Work?
Generative AI allows machines to generate new, and original content. It could be text/images/videos. In the enterprise world there are many applications to it. A simple example is if you’re using the project module of SAP, A Gen AI feature within the software can generate a project summary based on the current status of the project by analysing the budget/timeline/expense/tasks etc from the system. Otherwise as a PM you’re required to create an executive summary yourself by looking at multiple screens and data. Its immensely improving the productivity.
Generative AI uses deep learning techniques like neural networks to understand patterns in large datasets. Lets understand what are the key methods Gen AI uses to create content:
1. Generative Adversarial Networks (GANs)
One of the most revolutionary approaches in generative AI is the Generative Adversarial Network (GAN). GANs consist of two neural networks working in opposition:
Generator: This network generates new content, like an image of a face. The goal of the generator is to create data that is indistinguishable from real data.
Discriminator: The role of the discriminator is to evaluate whether the generated content is “fake” (generated) or “real” (from real datasets). Over time, the discriminator improves at distinguishing between real and fake, forcing the generator to create more realistic data.
These networks are locked in a constant competition, resulting in highly realistic outputs over time. For example, in image generation, the generator creates images that the discriminator judges as fake or real until the generator becomes proficient at mimicking realistic images.
This adversarial process has been groundbreaking, especially in fields like computer vision, where GANs can generate photorealistic images, transform images into different styles, or even create entirely new ones.
2. Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs) are another significant deep learning model in the generative AI landscape. Unlike GANs, VAEs focus on learning a compressed representation (or latent space) of the data. The process involves:
Encoder: Compresses the input data (e.g., an image) into a smaller, encoded representation.
Latent Space: A compressed version of the input that holds the essential features of the data.
Decoder: Reconstructs the original data from the compressed representation.
What makes VAEs unique is their ability to generate new data based on the learned latent space. For instance, after learning the core features of human faces, a VAE can generate entirely new faces that share characteristics of the faces in the dataset.
VAEs are useful in generating diverse outputs, ranging from new visual content to potential designs, and are known for their ability to interpolate between examples smoothly.
Conclusion
Generative AI is transforming industries by enabling machines to generate realistic, high-quality content. Techniques like GANs and VAEs are pushing the boundaries of what AI can do, making it an essential tool for enterprises.
Let’s explore together how SAP AI and generative models can drive your business forward. Keep learning!
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
The rapid advancements in artificial intelligence (AI), particularly with large language models (LLMs), have opened doors to more intelligent business solutions.
However, challenges like hallucination—where models provide incorrect or misleading information—present significant hurdles.
A key innovation addressing this issue is Retrieval-Augmented Generation (RAG). RAG combines the strengths of LLMs with real-time data retrieval, offering businesses a practical and cost-effective solution for contextual, grounded insights.
In this post, we’ll explore what RAG is, why it’s needed, and how SAP is enabling its customers to harness its power with real-life examples.
What is Retrieval-Augmented Generation (RAG)?
RAG is a framework that enhances traditional generative AI by integrating a retrieval mechanism.
When an AI model receives a query, it retrieves relevant information from a database or document repository, allowing the model to generate responses grounded in real-time, up-to-date information.
This process drastically reduces the risk of hallucinations, making the AI outputs more relevant and trustworthy for business applications.
In simple terms, RAG brings together two main components:
Retrieval: Accessing relevant documents or data in response to a query.
Generation: Using that retrieved information to generate a coherent and contextually appropriate answer.
Why is RAG Needed?
While LLMs have revolutionized AI-driven insights, they still face several limitations in real-world applications, particularly for businesses:
Hallucinations: LLMs may provide factually incorrect or irrelevant answers, especially when they’re disconnected from real-time or domain-specific data.
Costly Fine-Tuning: Training or fine-tuning LLMs with business-specific data is complex, expensive, and time-consuming.
Static Knowledge: LLMs, once trained, do not automatically update their knowledge, making them less useful for industries that rely on dynamic data.
By embedding a retrieval process, RAG reduces these challenges, ensuring that AI-generated responses are not only relevant but also grounded in the most recent and accurate data.
Why is RAG Relevant for SAP Customers?
SAP’s customers span industries such as supply chain management, HR, CRM etc, all of which depend heavily on accurate, up-to-date information.
For these businesses, the integration of RAG can significantly improve decision-making by:
Delivering reliable, grounded insights: With RAG, LLM outputs are based on the latest business data, offering contextualized and reliable responses.
Reducing response times: Business workflows often involve searching for information across various documents. RAG enables conversational AI tools to fetch this information in seconds.
Lowering Costs and Complexity: Instead of expensive fine-tuning of LLMs, businesses can use their existing data repositories to power RAG models.
How SAP is Enabling RAG
SAP has taken significant steps to incorporate RAG into its ecosystem, allowing businesses to build and deploy AI solutions grounded in their own data. Here are key elements of SAP’s approach:
SAP’s Central RAG Service
SAP provides a central, domain-agnostic RAG service that integrates with user interfaces, offering:
A scalable method to ensure AI systems use the most up-to-date information.
A framework to trace information sources, which helps verify AI-generated answers and build trust with end-users.
A vector engine in SAP HANA Cloud to store and query data embeddings efficiently .
Document Grounding in Joule and Generative AI Hub
SAP’s AI services like Joule—an AI copilot—incorporate document grounding, a core RAG technique:
Indexing: Documents are ingested, split, and stored as vector embeddings.
Retrieval: The system retrieves relevant data chunks for user queries.
Generation: This information is used by LLMs to create relevant and accurate responses .
Business Use Case: Using RAG in SAP SuccessFactors
Consider an HR team using SAP SuccessFactors. Traditionally, responding to policy-related inquiries could take 20 minutes per question, creating a heavy workload.
By implementing RAG through Joule, employees can now self-serve by querying policy documents directly. The system retrieves the most relevant document sections and generates a concise answer, reducing HR inquiry volume by 35% .
Conclusion
Retrieval-Augmented Generation (RAG) is revolutionizing the way businesses leverage AI by ensuring that AI-generated responses are contextual, relevant, and reliable.
SAP’s integration of RAG across its platforms, such as Joule and BTP, makes this technology accessible to its wide range of customers.
By reducing the need for costly LLM model fine-tuning and minimizing hallucinations, SAP is paving the way for its customers to benefit from powerful, trustworthy AI insights in their daily operations.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
At SAP TechEd 2024, SAP made several major announcements regarding its advancements in Generative AI. With a focus on simplifying business processes, enhancing customization capabilities, and bringing AI-driven insights to the forefront, SAP is paving the way for intelligent enterprise solutions powered by AI. Here’s a roundup of the key announcements that will reshape how businesses interact with SAP’s ecosystem.
1. SAP Build’s Generative AI Toolkit for SAP HANA Cloud
SAP has introduced a powerful Generative AI toolkit for SAP HANA Cloud, embedded within SAP Build. This new toolkit allows users to perform common data analysis tasks and handle even the most complex machine learning operations—no deep technical expertise required.
With the AI Toolkit for HANA Cloud, you can now leverage natural language prompts to execute sophisticated tasks. For example, if you need to create a time-series forecast model, you can simply ask Joule—SAP’s AI assistant—using plain language, and it will handle the rest. This reduces the need for understanding the intricacies of machine learning, making it easier for business users to incorporate AI-driven insights into their processes.
The integration of Joule and SAP Build Code simplifies the user experience, ensuring that even those with little to no coding experience can harness the power of AI in SAP HANA Cloud.
2. AI-Generated Recommendations in SAP Integration Suite
Another exciting update is the introduction of AI-generated recommendations in the SAP Integration Suite. When working on custom scripts, users can now open the script editor and generate optimization proposals with the help of Generative AI.
This new capability makes it possible to enhance and optimize custom integration scripts, ensuring that processes run more efficiently. By leveraging AI, users can save time and avoid common pitfalls, allowing them to focus on higher-level business logic instead of getting bogged down with technical details.
3. Joule Studio: Creating Custom Skills with Low-Code AI
Joule Studio, now part of SAP Build, is designed to unlock the power of Joule in building custom skills that integrate seamlessly with your business landscape. Joule Studio is a low-code environment that enables developers and business users to design and deploy AI-driven skills without needing to write complex code.
For example, if you want to pull service tickets from ServiceNow into Joule, you can use Joule Studio to create a custom skill with ease. By configuring the necessary parameters in ServiceNow APIs, you can deploy the skill and instantly access the data from within Joule. Similarly, you can integrate SAP SuccessFactors data and other business-critical information.
This capability empowers business users to create tailored applications that enhance decision-making and streamline operations. Joule Studio will be available in beta later this year and will become generally available (GA) soon after.
4. Generative AI Hub: New Models and Enhanced Capabilities
SAP’s Generative AI Hub is receiving significant upgrades, offering a broad array of state-of-the-art closed and open-source models, out of the box, integrated with SAP’s applications. This will give developers access to the best of both worlds: SAP’s real-time business data combined with foundational AI models from Mistral and Meta.
The Generative AI Hub will also introduce: – Orchestration services for managing multiple AI workflows. – Data masking and content filtering to ensure privacy and security. – Prompt templates and prompt management capabilities, making it easier to deploy AI-driven applications across different use cases.
Furthermore, the hub will come with SDKs for Java, JavaScript, and ABAP, ensuring that developers across different technologies can leverage SAP’s AI tools effectively.
5. SAP Knowledge Graph with SAP HANA Cloud Knowledge Graph Engine
The SAP Knowledge Graph is one of the most transformative tools in SAP’s AI offering. This new engine enables developers to ground Large Language Models (LLMs) in the entire semantic model of SAP, unlocking new potential for building intelligent business applications.
The SAP Knowledge Graph encompasses: – Over 450,000 ABAP tables. – More than 80,000 CDS views. – Over 7 million properties and fields.
This vast repository of structured metadata is essentially the bridge between natural language processing (NLP) and SAP’s highly structured data model. It allows developers to create more flexible use cases, such as advanced information retrieval, AI-based predictions, and analytics—all grounded in SAP’s data model.
By leveraging the SAP HANA Cloud Knowledge Graph Engine, developers can build business applications like never before, with real-time access to SAP’s rich data environment.
The SAP Knowledge Graph and its engine will be generally available in Q1 2025, offering unprecedented capabilities for AI-driven innovation within SAP landscapes.
Looking Ahead
SAP’s announcements at TechEd 2024 signal a major leap forward in how Generative AI will drive digital transformation. By combining cutting-edge AI models with SAP’s real-time business data, these new capabilities make it easier than ever for businesses to create smarter, more efficient processes.
From enabling non-technical users to generate machine learning models in SAP HANA Cloud to providing developers with advanced tools to create custom skills and integrate AI-driven insights, SAP is truly unlocking the power of AI across its ecosystem.
Stay tuned for more updates as these capabilities become available, and get ready to take your SAP solutions to the next level with Generative AI.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In our previous blogs, we explored various fundamental concepts surrounding artificial intelligence, including an introduction to AI, machine learning, deep learning, generative AI, large language models (LLMs), and prompt engineering. Each topic laid the groundwork for understanding the advanced capabilities of AI and its applications across industries.
Now let’s understand what’s SAP’s AI strategy.
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
SAP’s approach to AI is structured around its AI Strategy, which aims to infuse AI into applications and business processes through two main pillars:
Embedded AI in SAP Applications
Exposing Business-Centric AI Services via SAP BTP
Embedded AI in SAP Applications
SAP integrates AI capabilities natively within its business applications such as SAP S/4HANA, SuccessFactors, SAP Fieldglass etc. This allows customers to leverage AI without needing extensive development efforts.
For example, AI-assisted person insights in SuccessFactors helps managers identify discrepancies in employee compensation by analyzing historical data.
Similarly, the SAP Integration Suite now automates the creation of integration flows based on user-defined scenarios, streamlining the integration process.
In SAP Fieldglass, generative AI simplifies recruitment for external workers, enabling hiring managers to generate job descriptions 85% faster and project managers to deliver statements of work more efficiently.
Exposing Business-Centric AI Services
SAP is focused on offering targeted AI services through SAP Business Technology Platform (BTP) for custom scenarios. By partnering with AI leaders like OpenAI, NVIDIA, Microsoft etc SAP provides access to foundational models, enabling developers to build custom applications featuring generative AI.
Notably, SAP leverages Microsoft’s Azure OpenAI Service, allowing customers access to powerful LLMs without needing individual contracts with third-party vendors.
What is SAP Business AI?
You may have heard about SAP Business AI in multiple occasions and we want to simplify it for you. SAP Business AI encompasses a suite of AI-related solutions aimed at unifying SAP’s offerings.
You can image it as an umbrella under which all AI related solutions are included. It’s just an idea but not a single product. It includes multiple components.
Key components are:
Joule: An advanced generative AI copilot embedded throughout SAP’s cloud enterprise portfolio, delivering contextualized insights and enhancing productivity in a secure manner.
Embedded AI Capabilities: As discussed earlier these capabilities are embedded across SAP solutions, offering functionalities like intelligent document processing, personalized recommendations, and forecasting.
AI Foundation on SAP BTP: A collection of ready-to-use AI services and tools designed to accelerate AI integration into business processes.
What About SAP’s Own Foundation Models?
We’ve seen how SAP is using other foundation models like OpenAI’s to build AI solutions. But is SAP coming up with their own LLMs? the answer is Yes!
SAP is developing the SAP Foundation Model, a table-native AI model designed for predictive tasks on tabular data. Unlike LLMs, which excel in text generation, the SAP Foundation Model aims to outperform traditional methods in classification and regression tasks, thus ensuring reliable and relevant AI solutions tailored to business needs.
Conclusion
SAP Generative AI is not just about leveraging advanced technologies; it’s about embedding AI seamlessly into business processes and applications. By providing users with powerful tools and insights, SAP is committed to helping organizations unlock the full potential of AI to drive better outcomes.
Stay tuned for more insights as we continue our exploration of SAP Generative AI and its transformative impact on the business landscape.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
Now, we’re going to dive into the fascinating world of Prompt Engineering—a crucial skill that enables you to interact with Large Language Models (LLMs) like chatGPT.
Prompt engineering is the art and science of crafting the right inputs to get the most accurate, creative, and useful outputs from AI. It’s a key technique that unlocks the full potential of generative models, making it possible to leverage AI more effectively for a wide range of tasks.
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
Welcome to the next blog in our series on Generative AI! In this blog, we’ll dive into the fascinating world of Prompt Engineering, a crucial skill for working effectively with AI models, especially Large Language Models (LLMs) like ChatGPT.
What is Prompt Engineering?
Prompt Engineering is the process of crafting and refining the input prompts you give to AI models to produce the best possible responses.
Think of prompts as instructions or questions you provide to an AI, guiding it on how to generate the most relevant, accurate, and useful output.
The quality and clarity of your prompts can significantly influence the AI’s responses, making prompt engineering a vital skill for anyone working with advanced AI systems.
Have you ever used AI tools like ChatGPT and felt that the answers you received weren’t quite what you were looking for? Or maybe you’ve thought the responses from ChatGPT weren’t up to par?
Before concluding that the AI system isn’t performing well, consider that the issue might actually be with how you’re asking your questions or providing commands. The problem could be as simple as not knowing how to phrase your queries effectively.
Why is Prompt Engineering Important?
Accuracy and Relevance: Well-designed prompts help AI models generate more accurate and contextually relevant responses. By specifying what you want clearly and precisely, you increase the likelihood of getting the information or result you need.
Efficiency: Effective prompt engineering can save time and reduce the need for follow-up questions or clarifications. It streamlines interactions with AI, making the process quicker and more efficient.
Versatility: Mastering prompt engineering allows you to leverage AI models for a variety of tasks, from generating creative content to answering complex queries and providing detailed analyses.
How to Craft Effective Prompts
Creating effective prompts involves understanding the nuances of how AI models interpret language and context. Here are some key tips for crafting prompts that get the best results:
Be Specific and Clear: The more specific and clear your prompt, the better the AI can understand and respond. For example, instead of asking, “Tell me about cars,” you might ask, “What are the key features of electric cars compared to traditional gasoline cars?”
Provide Context: Adding context to your prompts helps the AI understand the background or intent behind your query. For instance, if you’re asking for advice on a project, provide details about the project’s goals and constraints.
Use Structured Prompts: For complex queries, structuring your prompt into multiple parts or steps can improve clarity. For example, “First, summarize the benefits of renewable energy. Then, explain how these benefits impact the economy.”
Experiment and Iterate: Don’t be afraid to experiment with different phrasings or approaches. If the initial prompt doesn’t yield the desired result, try rephrasing or providing additional details.
Consider the Model’s Strengths and Limitations: Understand the capabilities and limitations of the AI model you’re working with. Tailor your prompts to leverage the model’s strengths and avoid areas where it might struggle.
Examples of Effective Prompt Engineering
Generating Creative Content:
Poor Prompt: “Write a story.”
Improved Prompt: “Write a short story about a young girl who discovers a hidden magical world in her backyard.”
Answering Specific Questions:
Poor Prompt: “Tell me about climate change.”
Improved Prompt: “Explain how human activities contribute to climate change and suggest three ways individuals can reduce their carbon footprint.”
Summarizing Information:
Poor Prompt: “Summarize this article.”
Improved Prompt: “Summarize the main points of this article on the impact of social media on mental health, focusing on both positive and negative effects.”
Summary
In this blog, we explored the essentials of Prompt Engineering, a key skill for effectively interacting with AI models like ChatGPT. Prompt Engineering involves crafting and refining the questions or instructions you give to AI to generate the best possible responses.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
From the previous blogs you’ve learned the basics of Artificial Intelligence (AI), explored how machines can learn through Machine Learning (ML), taken a deeper dive into Deep Learning (DL), and discovered how Generative AI can create new and original content.
Now, we’re going to take it a step further by focusing on Large Language Models—a specific and revolutionary application of Generative AI that is transforming how machines understand, process, and generate human language. Let’s explore how all the concepts you’ve learned so far come together to power these cutting-edge models!
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
How Do Large Language Models Fit Into Generative AI?
Imagine you’re talking to a super smart robot, like ChatGPT, that can understand what you say and talk back to you in ways that make sense. How does it do that? Well, it uses something called a Large Language Model (LLM).
Think of the LLM like the brain of the robot. It helps the robot understand the words you say and come up with answers that sound like a human. So, whenever a robot, like ChatGPT, needs to create human-like sentences, it uses this special brain—the Large Language Model—to help it do that.
What is a Language Model?
A Large Language Model (LLM) is like a super smart robot brain that helps computers understand and talk like humans. It works by analyzing patterns in text and using statistics to predict which words are likely to come next in a sentence.
Imagine you’ve read hundreds of books, stories, and conversations, and you remember a lot about how words fit together. That’s what an LLM does! It has read tons of text from the internet, books, and other places, so it knows a lot about how people talk and write.
Here’s how it works:
Learning from Text: The LLM learns by looking at millions and millions of sentences. It sees which words are used together, how sentences are formed, and what makes sense in different situations.
Predicting the Next Word: When you type something like, “I would like to eat…” the LLM guesses what comes next, like “pizza,” “sushi,” or “ice cream,” based on all the patterns it has learned from reading.
Answering Questions: If you ask the LLM a question, like “Why is the sky blue?” it uses what it learned from reading to give you a helpful answer. It doesn’t “think” like a person, but it can find patterns in the text to give you a smart-sounding response.
Getting Better Over Time: The more text it reads, the smarter the LLM gets at predicting and understanding what people mean. It’s kind of like how you get better at reading or solving puzzles the more you practice.
So, an LLM is a super tool that helps computers understand and talk to people by learning from lots of words and sentences!
Let’s take an example to understand better!
When we use messaging apps on our phone, they help by predicting the next word as we type. For example, if you type “I would like to eat,” the phone might suggest words like “pizza,” “sushi,” or “ice cream” because it knows these are common food choices people mention after saying that phrase.
The phone makes these predictions based on the words you’ve typed and what it has learned from analyzing lots of text. It guesses what you’re likely to say next by recognizing patterns in everyday language.
LLM and NLP (Natural Language Processing)
LLMs are an advanced form of Natural Language Processing (NLP). NLP allows computers to understand and interact with humans using natural language, like English. Think of virtual assistants like Siri or Alexa, which can understand commands like, “Set an alarm for 7 AM.” NLP makes this possible by breaking down the sentence, understanding its meaning, and responding in a way that makes sense.
Where Are LLMs Used?
LLMs are used in many AI applications across industries, including:
Virtual Assistants: Siri, Alexa, and Google Assistant use LLMs to understand commands and respond accurately.
Chatbots: Models like ChatGPT help answer questions, engage in conversation, and provide support in human-like ways.
Language Translation: Tools like Google Translate use LLMs to translate text between languages while keeping the meaning intact.
Text Generation: LLMs can write articles, product descriptions, or even creative stories based on user prompts.
Summarization: They can summarize lengthy documents into short, easy-to-understand paragraphs.
Sentiment Analysis: LLMs analyze text to detect emotions, like whether a customer review is positive or negative.
Popular Large Language Models
GPT (Generative Pre-trained Transformers): Created by OpenAI, GPT is one of the most well-known LLMs, powering ChatGPT.
BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT helps with tasks like understanding search queries.
LaMDA (Language Model for Dialogue Applications): Another model from Google, designed specifically for conversations.
Summary
Large Language Models are the backbone of many AI applications today, making it easier for machines to understand and communicate with humans in natural language. By learning from vast amounts of text, LLMs can predict, generate, and summarize text with remarkable accuracy.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In our previous blog, we talked about how Artificial Intelligence (AI) helps a robot find your lost toy by thinking like a human. Now, we’re going to take it a step further and explore Machine Learning (ML), which is like teaching the robot how to learn on its own. Let’s dive into it in a fun and simple way!
This 7 part blog series is designed for anyone who wants to start learning about SAP AI. No matter what your job is or what you do, understanding the basics of Generative AI how it connects to SAP is a smart choice.
In this blog series, we’ll break down SAP AI into easy-to-understand parts, covering one step at a time.
In our previous blog, we discussed how AI can help a robot find your lost toy. Now, let’s explore how we can teach the robot to recognize different types of toys.
Imagine we want the robot to be able to identify various toys — like cars, dolls, and building blocks. Here’s how we can do it:
Show and Tell: We start by showing the robot lots of pictures of different types of toys. For example, we show it many pictures of cars, dolls, and building blocks, each labeled with the toy’s name.
Teach the Robot: We need to train the robot to recognize these toys based on their size, color, shape, and other features. This is like teaching a friend what your favorite toys look like so they can help you find them.
Recognize New Toys: Once the robot has seen many pictures of each type of toy, it can start to recognize them in real life, even if they don’t look exactly like the pictures. For example, even if you have a new toy car that looks a bit different from the ones we showed, the robot should still be able to identify it as a toy car.
Practice Makes Perfect: The more pictures of toys the robot sees, the better it becomes at recognizing them. This is because it learns to pick out common features and differences between toys, helping it make better guesses.
This process of teaching the robot with lots of examples is known as Machine Learning.
In summary, Machine Learning is:
A subset of Artificial Intelligence: It’s one way of making machines smart.
Learning from Examples: It involves giving machines lots of data (like pictures of toys) so they can learn and make decisions based on that data.
So, just like you learn to recognize different toys by seeing and playing with them, robots learn to identify toys by looking at many examples. The more they see, the smarter they get!
Types of Machine Learning
Now, let’s dive into the different ways robots and computers can learn from data. Machine Learning can be categorized into three main types:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Each type of Machine Learning serves a unique purpose and uses different methods to learn from data. Let’s explore each type in detail.
Supervised Learning
Remember when we taught the robot to identify various toys by showing it lots of pictures of different types of toys, each labeled with the toy’s name? This method is known as Supervised Learning.
In Supervised Learning:
We Provide the Answers: Just like you told the robot which toy is a car, doll, or block, we provide the machine with examples that are labeled with the correct answers.
The Machine Learns: The machine uses these labeled examples to learn how to identify or predict new, unlabeled data.
Here’s how Supervised Learning works in the real world:
Email Spam Filtering: Classifying emails as spam or not spam based on features from the email content and sender.
Image Classification: Identifying objects in images, such as recognizing animals or detecting objects in self-driving cars.
Facial Recognition: Verifying individuals based on facial features, used in security systems or unlocking devices.
Financial Fraud Detection: Spotting potentially fraudulent transactions by analyzing patterns in financial data.
Speech Recognition: Converting spoken language into text, like in voice assistants such as Siri or Google Assistant.
Unsupervised Learning
Let’s think about how kids learn to group their classmates on the first day of school. They notice differences and similarities among their classmates without anyone explicitly telling them how to categorize them. This is similar to Unsupervised Learning.
In Unsupervised Learning:
No Labels Provided: The machine is given data without any labels or categories. It must find patterns and group similar items on its own.
Pattern Discovery: The machine identifies similarities and differences in the data, grouping similar items together based on the patterns it discovers.
For example:
Clustering Customer Segmentation: Businesses use unsupervised learning to group customers based on purchasing behavior, helping tailor marketing efforts.
Anomaly Detection in Cybersecurity: Identifying unusual patterns in network traffic to flag potential security threats.
Recommendation Systems: Suggesting products or content based on patterns in user behavior, like how Netflix recommends movies.
Reinforcement Learning
Imagine teaching a dog a new trick. You give the dog a treat when it performs the trick correctly and no treat when it doesn’t. Over time, the dog learns to do the trick more often to get more treats. This is similar to Reinforcement Learning.
In Reinforcement Learning:
Learning by Trial and Error: An agent (like a robot or computer program) learns to make decisions by receiving rewards for good actions and penalties for bad ones.
Interactive Learning: The agent interacts with its environment, learns from the outcomes of its actions, and adjusts its behavior to maximize rewards.
For example:
Game Playing: AlphaGo, developed by DeepMind, uses reinforcement learning to play the board game Go at a superhuman level, learning the best strategies through practice.
Self-Driving Cars: Reinforcement learning helps these cars learn how to navigate traffic and make driving decisions by simulating various driving conditions.
Algorithmic Trading: Making trading decisions based on historical and real-time market data to develop effective trading strategies.
Summary
Machine Learning is a branch of Artificial Intelligence that allows computers to learn from data and experiences, without being explicitly programmed for every task. Whether it’s recognizing toys, understanding voice commands, or helping doctors analyze medical images, Machine Learning is becoming an integral part of our everyday lives.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
The integration of SAP Artificial Intelligence (AI) into various business operations is transforming how companies approach efficiency, accuracy, and strategic insights. From finance to supply chain management, procurement, human resources, sales, marketing, and industry-specific processes, SAP AI is proving to be an invaluable asset.
Finance with SAP AI
SAP AI is revolutionising financial processes by automating tasks such as invoice matching, auditing, payments, and expense management.
This leads to reduced days sales outstanding and simplifies error resolution through intelligent guidance. SAP AI also helps to prevent financial fraud by detecting anomalies that might be missed by humans.
Let’s have a look at some of the real world examples:
Intelligent Invoice Matching:
SAP Cash Application uses machine learning to automate the matching of incoming payments to invoices, which reduces manual effort, lowers days sales outstanding, and enhances the quality of work related to accounts receivable.
It can automatically learn from accountants’ actions to improve matching accuracy.
Predicting Late Payments:
AI analyses accounts receivable data to predict late payments, allowing for more effective collection activities. This reduces collection costs and minimises write-offs.
Enhanced Financial Close:
SAP AI improves the goods receipt/invoice receipt (GR/IR) account reconciliation processes, leading to reduced processing times during financial close. It can also help resolve common errors during the financial close using a step-by-step guide.
Tax Compliance: AI streamlines and automates tax compliance checks, learning from manual decisions to increase automation rates over time.
Data Analysis: AI tools can rapidly analyse large volumes of data, detect discrepancies and identify trends.
Planning and Strategy:
AI supports planning and strategy by making forecasts and automating budgeting processes with predictive analytics.
AI enriches planning models with historical data from source systems and external market conditions, benefitting from predictive scenarios.
Decision-Making:
AI enhances decision-making in finance, with tools available to provide data-informed recommendations. With the ‘just ask’ feature, users can search data using natural language and get answers in the form of charts and tables.
Supply Chain with SAP AI
In supply chain management, SAP AI facilitates a more responsive approach to customer demand and production adjustments.
Here are some of the popular use cases from SAP AI:
Demand Forecasting:
SAP AI can predict customer demand using real-time information and historical data. This helps reduce inventory costs and maximize resource utilisation.
SAP Integrated Business Planning uses machine learning and statistical models to generate accurate demand plans.
Quality Control:
AI-enabled visual inspections and anomaly detection improve quality assurance and help to mitigate risks before they escalate. Machine learning can be used for visual inspections, enhancing production efficiency with automated planning and dispatching.
Predictive Maintenance:
SAP AI can predict equipment failure using IoT data and send warnings to maintenance and operations. This enables proactive maintenance, reduces downtime, and uses machine learning to analyse failure modes.
Optimised Logistics:
AI automatically derives slotting rules from product data in warehouses and generates statistical models from system data. It can also automate the processing of goods receipts and delivery notes, improving operational efficiency and data quality.
Product Development:
AI enhances product development by connecting 2D or 3D product models to business information and process-related data. It helps to reduce time spent on tagging master data to 3D visualisation objects by automatically identifying and assigning master data to components.
Risk Management:
AI helps evaluate supply chain risks and projected delays, helping businesses focus on the most critical shipments. It can also improve demand forecast accuracy by considering multiple factors such as inventory levels and weather changes.
Procurement with SAP AI
SAP AI transforms procurement into a proactive, data-driven operation. Here are a couple of examples:
Automated Compliance: Automated systems provide on-screen recommendations to ensure purchasing compliance.
Data Extraction: AI streamlines data extraction and minimises manual effort, reducing errors and improving decision-making. It provides prescriptive guidance based on historical data and trends.
Human Resources with SAP AI
AI in human resources fosters a dynamic and inclusive workforce environment. Lets find out more use cases in HCM.
Talent Acquisition:
AI enhances talent acquisition by matching candidates with suitable job opportunities and improves the employee experience using digital assistants for real-time help.
SAP AI can also quickly generate job descriptions and interview questions, identify qualified candidates, and speed up the hiring process.
Employee Engagement and Retention:
By personalising experiences and providing tailored career growth recommendations, SAP AI creates a more positive and productive work environment, increasing employee satisfaction.
Efficiency:
SAP AI streamlines HR processes, such as compensation discussions, candidate assessment, and job description creation, by up to 90%. It allows HR to focus more on strategy and less on routine tasks.
Sales and Marketing with SAP AI
SAP can personalise customer interactions with AI by tailoring product recommendations and enhancing lead scoring to strengthen sales pipelines.
Personalised Experiences:
SAP AI helps businesses understand and respond to customer behaviour, minimise churn, and maximise upselling opportunities. AI marketing automation can target customers based on lifecycle stage, engagement propensity, or estimated spend.
Product Recommendations:
SAP AI recommends products based on customer interactions, purchase history and browsing patterns. SAP Intelligent Product Recommendation uses historical data and machine learning to simplify price quotations and recommend products based on customer needs.
AI-Driven Content:
Generative AI can be used to tailor email subject lines and preview text, as well as enrich product descriptions and tags.
Audience Segmentation:
Its key to segment audience to reach out to them with more personalised messages. SAP AI is enabling faster creation of audience segments using natural language and generative AI.
Sales Efficiency:
AI identifies high-potential leads and guides sales teams, optimising sales efforts and customer engagement strategies.
SAP AI can also generate tailored information to enhance the sales team’s understanding of accounts and leads, as well as recommend next actions and email drafts.
Shopping Assistance:
It might to new for you but, SAP AI shopping assistants use natural language to help customers find products online.
Industry-Specific SAP AI Applications
Industry-Specific SAP AI Applications
AI is versatile, and it’s proving its value across industries by simplifying complex processes and modernizing operations.
Agriculture:
Farmers can make better growing decisions with SAP AI. It analyzes weather patterns, soil conditions, and market trends, offering insights that promote sustainable farming. With SAP Intelligent Agriculture, the plan-to-harvest process becomes more efficient.
E-Mobility:
Managing electric vehicle fleets is easier with AI. It identifies the best charging times to lower costs and prevent overloading the power grid. SAP E-Mobility helps businesses manage their EV charging networks smoothly.
Retail:
Retailers are turning to AI for data-driven decisions. From predicting demand to automating processes, SAP AI personalizes product recommendations and optimizes merchandising. It ensures customers see the products that matter most to them.
Manufacturing:
SAP AI takes the guesswork out of production. It improves efficiency through better planning and automates dispatching, allowing manufacturers to streamline their operations.
Trade Promotion:
SAP is making trade promotions more effective. It uses AI forecasting models to predict the outcomes of promotional plans, helping businesses optimize their spending. SAP Revenue Growth Management ensures that trade promotions are planned and executed efficiently.
Looking Ahead
The application of SAP AI across these critical sectors underscores its transformative potential in business.
By automating complex processes, providing insightful data analysis, and enhancing decision-making capabilities, SAP AI is not just a tool for innovation—it is a foundational element that propels businesses towards a more efficient, accurate, and insightful future.
As industries continue to embrace SAP’s AI solutions, the scope for growth and improvement expands, paving the way for a new era of business operations that are smarter, more responsive, and continuously evolving.
SAP will continue to reshape human roles and responsibilities with AI, creating new tasks and shifting the focus of work toward strategy and analysis.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
SAP launched SAP Business Technology Platform (SAP BTP) as a comprehensive solution that serves as the technical backbone of the entire SAP ecosystem. In this blog, we’ll break down SAP BTP in a straightforward manner, focusing on its key aspects and benefits.
Let’s Dive In!
Imagine you’re a developer at an SAP partner or customer tasked with building a new cloud solution and expanding an existing SAP solution. Let’s explore what developers typically wish for in such scenarios and how SAP BTP addresses these needs.
Simplified Integration
Seamless integration capabilities are essential to minimize time spent on integration and system maintenance.
→ SAP BTP offers the “SAP Integration Suite,” a comprehensive set of services facilitating seamless integration of SAP and non-SAP systems and applications.
Focus on Business Logic
Developers prefer frameworks that allow them to concentrate on solving business challenges rather than dealing with technical intricacies.
→ SAP BTP introduces frameworks like “SAP Cloud Application Programming Model,” empowering developers to focus solely on business logic while the framework handles other aspects.
Platform Flexibility
You need a Platform-as-a-Service (PaaS) that allows you to swiftly build and deploy cloud solutions without local installations, supporting various programming languages.
→ SAP BTP serves as a PaaS offering three environments: Cloud Foundry, ABAP, and Kyma. These environments enable you to choose from any programming language and effortlessly build and deploy cloud solutions.
Ready-Made Development Environment
Developers require a pre-configured development environment with all essential plugins for seamless coding.
→ SAP Business Application Studio is offering a ready-made development environment where you can start coding without the hassle of installing or configuring anything.
Simplified Build and Deployment
Efficient builds and deployments are crucial. Developers seek tools that streamline these processes.
→ SAP Business Application Studio in combination with MTA Build Tool (MBT) and BTP CLI, simplifies build and deployment tasks, eliminating unnecessary complexities.
Comprehensive Database Solutions
A robust Database-as-a-Service (DBaaS) is indispensable for storing and processing vast amounts of structured and unstructured data, providing real-time access.
→ SAP BTP offers SAP HANA Cloud, a cloud-native variant of SAP HANA database, catering to diverse data requirements in a single service.
In Summary for organizations aiming to build or extend cloud solutions with minimal effort, cost-effectiveness, and faster time-to-market, SAP BTP emerges as the ideal solution. It eliminates the need for extensive setup, installation, configuration, and maintenance, offering a portfolio of SAP solutions and services under one umbrella.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
SAP’s approach to AI goes beyond the usual. SAP said it will spend 2 billion euros on AI, which is huge. SAP doesn’t see AI just as an add-on but as a key part of its business software, smoothly built into it.
This integration of GenAI technology with Business Applications is more than just an improvement; it marks a major change towards smarter business processes, ready to change the way companies work, make decisions, and enhance their performance.
The Dawn of Generative AI in Business
SAP’s foray into generative AI is driven by a vision to deliver unmatched value to its customers. By contextualizing generative AI within business applications, SAP aims to unlock the potential that was once deemed unattainable. The focus is on creating tangible benefits that resonate across various industries.
In a series of recent announcements, SAP has outlined its roadmap for generative AI and its commitment to innovation. The launch of a generative AI hub at the end of 2023, and the introduction of the SAP HANA Cloud Vector Engine, expected by March 2024, are testament to SAP’s strategic investment in AI technology.
Joule: The Next-Generation Digital Assistant
A standout innovation in SAP’s AI portfolio is Joule, it was launched in Sep 2023, a generative AI-powered digital assistant. Joule represents a leap forward in how businesses interact with AI, offering personalized assistance for daily tasks.
Joule is embedded with SAP’s enterprise cloud portfolio. It is not merely an addition to the existing tools but a transformative natural-language, generative AI copilot designed to revolutionize the efficiency and effectiveness of businesses across various sizes and sectors. This innovation promises to integrate seamlessly into daily operations, leveraging the power of artificial intelligence.
SAP’s Generative AI Architecture: A Blueprint for Innovation
At the heart of SAP’s generative AI strategy lies a deep understanding of business processes, which guides the integration of AI across its application portfolio.
The architecture below shows how Generative AI is placed through SAP’s Cloud portfolio. It comes with the tools and libraries that you need to build and customize the solution.
SAP is developing a wide range of use cases from question answering, text generation, classification, and exploring the potential of emerging paradigms such as LLM agents.
Looking Ahead: SAP’s Vision for the Future
SAP’s vision for the future of generative AI in business applications is both ambitious and forward-thinking. The company plans to continue enhancing its platform, integrating generative AI in innovative ways that go beyond current applications. This includes the development of writing assistants, intelligent process recommendations, and other AI-driven tools designed to empower businesses. SAP’s roadmap for generative AI reflects a commitment to staying at the forefront of technology, driving progress, and delivering value in the ever-evolving digital landscape.
In conclusion, SAP’s integrated approach to generative AI is not just a strategic move; it’s a vision for the future of business technology. By embedding AI into the fabric of business applications, SAP is setting a new standard for intelligence, efficiency, and innovation. As companies navigate the complexities of digital transformation, SAP’s generative AI solutions stand as a beacon of possibility, offering a glimpse into a future where businesses are smarter, more agile, and more connected than ever before.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
SAP Build Code was announced at SAP Teched in Bangalore on 2nd Nov 2023. With a bunch of already existing tools from SAP to build applications in cloud SAP Build Code raises the eyebrows of developers. Here in this blog, we’re going to answer FAQs on this topic to help you better.
What is SAP Build Code?
SAP Build Code is a tool that uses Generative AI for developing applications. It’s designed for building and extending SAP solutions in the cloud. The tool provides a cloud-based experience similar to working on a desktop, making it easy to build applications and extend SAP solutions.
What are the key features of SAP Build Code?
GenAI: Access generative AI capabilities to generate data models, services, sample data, and UI annotations using natural language prompts.
Build Lobby: Start new projects with a template through the SAP Build lobby to provide a framework for developers.
Best Practices for CAP: Develop CAP-based applications following best practices with the help of guided tutorials.
Mobile Apps: Build mobile apps by accessing SAP mobile services from SAP Build Code.
Pre-packaged content: use pre-packaged integrations, APIs, business services, and templates from the integrated service center.
Testing: Generate unit tests for both positive and negative scenarios.
Rapid Development: app logic, data models, services, and sample data can be generated using AI code generation with natural language descriptions.
What programming languages are supported by SAP Build Code?
SAP Build Code supports Java and JavaScript.
What kinds of applications can be built using SAP Build Code?
You can use SAP Build Code to create full-stack applications, user interfaces, and mobile apps using the SAP Cloud Application Programming Model (CAP).
When will SAP Build Code be available to customers and partners?
SAP Build Code is expected to be available by early 2024.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
Let’s take a closer look at SAP BTP, a new thing SAP introduced in 2021. But did you know SAP has been doing platform business for a while? About seven years ago, they started with something called SAP HANA Cloud platform, which later became SAP Business Technology Platform.
In this blog, we’ll break down SAP BTP for beginners.
What is SAP Business Technology Platform?
There are mainly 2 scenarios we can use SAP BTP. Let me explain it to you with examples.
Example 1: You’re not an SAP Customer
You are a startup or a company with a need to build an app in the cloud. You’ve the requirements to integrate the app with different applications and also need to use the latest technologies like AI, Analytics, Automation etc.
Example 2: You’re an SAP Customer
You’re an SAP customer using one or more products of SAP. And your business has a requirement to build new business process which is not available in SAP as a standard functionality. But it’s a critical custom requirement for your business. It should be scalable, independent and should not impact the underlying SAP processes.
In both examples, you can use SAP BTP to build your applications. So, what does SAP BTP offers to let you build the apps?
5 Pillars of SAP BTP
What Makes SAP BTP Unique from Other Platform Vendors like Azure, AWS?
There are 3 main reasons that make SAP BTP unique from other vendors:
Built by SAP for SAP Customers
If you look at each service on SAP BTP it was built with SAP Customers in mind. SAP knows its customer’s industry better than any other vendors. Hence, each service is tailored to meet the custom requirements of SAP customers. Seamless integration with existing SAP systems is one good example.
Multi Cloud Environment (Open Platform)
SAP BTP is an open platform that allows businesses to run their applications and processes on various cloud infrastructures like Azure, AWS or Google. That means you have more flexibility and options for where you want to deploy your apps.
Business Centric
SAP BTP is designed with a strong focus on business applications, particularly for enterprises using SAP software. Application development tools and services are tailored for building SAP-specific applications. While other platforms offer more generalized cloud services.
Keeping the Core Clean
Keeping the core clean means minimizing customization and modification of the core SAP ERP.
In the cloud, customization comes with a big price. Because cloud infrastructure is maintained by SAP and customers don’t have control over it. Regular updates in the cloud will impact the customization.
Instead, customers can leverage SAP BTP to build customizations. Here are some reasons why you shouldn’t touch the core and use SAP BTP for your extensions:
Easier Upgrades
When the core system is kept as close to the standard, it becomes much easier for upgrades. This reduces the risk of breaking customizations during an upgrade.
Lower TCO
Customizations often require more effort to develop, test, and maintain. By leveraging the SAP BTP for custom solutions, you can potentially reduce the TCO compared to heavily customizing the core system.
Better Security
Customizations can sometimes introduce security vulnerabilities. Keeping the core clean and using the SAP BTP for custom applications allows you to ensure that all your solutions adhere to security standards.
Overall, keeping the core clean by utilizing SAP BTP enables you to maintain an efficient SAP landscape, and you get easier SAP support!
SAP BTP Job Opportunities are Immense
SAP BTP consists of many services and no single person can handle it. You can become an architect if you’ve vast experience in designing and building apps or you can become a consultant for a particular service(integration, SAPUI5 etc). Lastly, if you were a Basis Admin you can choose SAP BTP Administration as your next career path.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
SAP Mobile Service is one of the oldest services in SAP BTP(Business Technology Platform). It was earlier called HCP Mobile Services. It is a platform that provides everything you need to build mobile apps for your enterprise. It has advanced features like the ability to work without internet(offline feature), usage analytics, and seamless integration with SAP systems.
It is used mainly for B2B scenarios. And depending on the complexity of the requirement it provides different ways to build the app.
The options provided by the platform to build the app are:
SAP Mobile Cards
Mobile Development Kit(MDK)
SDK for Android
SDK for iOS
Each option is different in the way you are building the app and the skills you need to build it. For example, native apps for Android and iOS are build using SDK for Android and iOS. And you need skills in Java and SWIFT to build Android and iOS apps respectively. At the same time Mobile Cards and MDK don’t require good programming skills.
Choosing the right option to build app is a critical decision. It purely depends on the complexity of the app you’re trying to build. Make sure you’re not making a mistake here!
Mobile Cards and MDK don’t require high programming skills. If your apps are not complex and don’t require the highest performance or best user experience you can choose these. For mobile apps with many screens and complexity go for the Native approach that needs high programming skills – but worth it!
Many SAP customers are using Mobile Services to build their enterprise apps. Because of its easy integration with SAP systems and post go-live customers can talk to a single vendor(SAP) for support. If a different platform is used(ex. Azure) to build the mobile app that talks to SAP systems, supporting those apps will be challenging because the customer has to talk to two different vendors when an issue occurs.
The solution and the features are mature considering the number of years the service has been available for and the large number of customers using the service. Compared to other platforms (Azure, Google) that provide similar services to build mobile apps it’s worth choosing SAP’s own platform for building mobile apps if your requirement is B2B and it integrates with SAP Systems.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
In the dynamic landscape of enterprise systems, the need for seamless connectivity between on-premise systems and cloud-based applications is crucial.
SAP Cloud Connector emerges as a solution to bridge this gap. It is facilitating a secure and efficient communication between SAP landscapes in corporate data centers and the SAP Business Technology Platform(BTP).
What is SAP Cloud Connector?
SAP Cloud Connector is a lightweight, yet powerful, software designed to establish a secure connection between on-premise systems and the SAP BTP. This connectivity enables bidirectional communication, allowing businesses to integrate on-premise data and applications with cloud-based services offered by SAP.
When you are building applications in the cloud using SAP BTP one of the primary requirements is connectivity with the backend(ERP). SAP Cloud Connector solves that problem.
How to setup SAP Cloud Connector?
It is a small software that you can install in a server or on your laptop. Once it is installed you are required to provide your BTP details and the ERP application details to establish connection.
Once the setup is complete you will be able to test the connection to confirm the communication between SAP BTP and on premise.
Why is SAP Cloud Connector Needed?
Data Integration
Facilitates seamless integration of on-premise data and applications with cloud-based services, enabling a unified and cohesive business environment.
Real-time Data Access
Provides real-time access to on-premise data, allowing organizations to leverage the advantages of cloud-based analytics, machine learning, and other advanced services.
Through TLS (Transport Layer Security) it ensures a secure communication channel. It also provides user authentication and authorization mechanisms to control access to resources.
When is SAP Cloud Connector Not Needed?
While SAP Cloud Connector is essential for many scenarios, there are cases where it may not be required:
Purely Cloud-Based Deployments
If an organization operates solely in the cloud without any on-premise systems, the use of SAP Cloud Connector may be unnecessary.
Limited Integration Requirements
For businesses with minimal integration needs between on-premise and cloud environments, alternative methods such as SAP Cloud Platform Integration services might suffice.
SAP Cloud Connector plays a pivotal role in enabling organizations to harness the benefits of both on-premise and cloud-based systems. Its robust security protocols ensure that data remains protected during transit.
P.S If you’re keen to grow your SAP career with AI Expertise irrespective of your SAP domain checkout this SAP AI Masterclass on Udemy crafted by Ex-SAP employees!
